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Free Questions on Databricks Certified Data Engineer Professional Certification
Here are some top 30 Databricks Certified Data Engineer Professional Exam FREE Questions for you:
Domain: Databricks Tooling
Question 1. A data engineer is developing a highly sophisticated Databricks notebook that performs advanced data analysis tasks on large datasets. She wants to incorporate a seamless interactive experience for users into the notebook so they can dynamically control some important analysis-related parameters. The correct setting of these parameters has a significant impact on the analysis’s accuracy and effectiveness. Which method from the list below should the data engineer use to use Databricks widgets to fulfill this requirement?
A. Use the dbutils.widgets.dropdown() function to create a dropdown widget with a wide range of parameter options. Register an intricate event handler that captures the selected value and triggers the corresponding analysis logic, ensuring real-time responsiveness and efficient processing.
B.Use the spark.conf.set() function to set global configuration variables for the critical parameters. Craft a complex user interface using HTML and JavaScript within the notebook, allowing users to manipulate the parameters and dynamically update the configuration variables. Leverage the power of JavaScript event handling to ensure seamless interaction and accurate analysis.
C. Utilize the displayHTML() function to render an elaborate and visually appealing HTML form within the notebook. Implement intricate JavaScript logic within the HTML form to capture the user’s input for the critical parameters. Leverage the power of JavaScript frameworks like Vue.js or React to provide a highly interactive experience, dynamically adjusting the analysis parameters and triggering the analysis process.
D. Employ the dbutils.widgets.text() function to create a text input widget with advanced validation capabilities. Develop a complex input validation mechanism that ensures the user’s input adheres to specific criteria and is compatible with the analysis requirements. Retrieve the validated input value using the dbutils.widgets.get() function and utilize it to dynamically control the critical parameters during the analysis.
Correct Answer: A
Explanation:
Databricks Widgets are interactive components that can be used in Databricks notebooks to improve the user interface and offer dynamic control over settings and parameters. They let users interact with the notebook in real-time by entering values, selecting options, and more.
Option A is correct. It advises making a dropdown widget using the dbutils.widgets.dropdown() function. With this strategy, users can choose parameter options from a dropdown list for a fluid and simple interactive experience. The chosen value can be recorded and used to activate the associated analysis logic by registering an event handler. This method is the best fit to meet the requirement because it guarantees real-time responsiveness and effective processing.
Option B is incorrect. It suggests using a complex HTML and JavaScript user interface along with the spark.conf.set() function to set global configuration variables. Although this method gives users the ability to change certain parameters, it does not offer seamless integration with the notebook environment. Furthermore, depending on JavaScript event handling for parameter updates can add complexity and raise the possibility of performance problems. It does not make use of Databricks widgets’ unique abilities, which are created for interactive notebook experiences.
Option C is incorrect. It suggests building a complex HTML form with JavaScript logic for parameter capture using the displayHTML() function and JavaScript frameworks like Vue.js or React. Although this strategy can offer an interactive experience, it adds to the complexity and relies on outside JavaScript frameworks. It might need more setup and might not make use of all the features that Databricks widgets come with by default.
Option D is incorrect. It suggests building a text input widget with validation capabilities using the dbutils.widgets.text() function. Although this method enables parameter input from users, it lacks other alternative interactive features. Furthermore, creating a complicated input validation mechanism requires a lot of work and might add more complexity. It doesn’t offer the same degree of fluid interaction and usability as the right answer.
Reference:
https://docs.databricks.com/notebooks/widgets.html
Domain: Databricks Tooling
Question 2. The following operations must be carried out by a script that needs to be written using the Databricks CLI:
Creates a new cluster with a specific configuration.
Uploads a set of Python files to the cluster.
Executes a Python script on the cluster.
Captures the output of the script execution and saves it to a local file.
Which of the following commands can be used in the Databricks CLI script to accomplish these tasks efficiently?
A. databricks clusters create –cluster-name “my-cluster” –node-type “Standard_DS3_v2” –num-workers 4
databricks workspace import_dir /local/path/to/python/files /dbfs/mnt/python/files
databricks jobs create –name “my-job” –existing-cluster-id <cluster-id> –python-script /dbfs/mnt/python/files/cli_script.py
databricks jobs run-now –job-id <job-id> –sync
B. databricks clusters create –cluster-name “my-cluster” –instance-profile “my-profile” –num-workers 4
databricks fs cp /local/path/to/python/files dbfs:/mnt/python/files
databricks jobs create –name “my-job” –new-cluster spec-file:/path/to/cluster-spec.json –python-script dbfs:/mnt/python/files/cli_script.py
databricks jobs run-now –job-id <job-id> –sync
C. databricks clusters create –cluster-name “my-cluster” –node-type “Standard_DS3_v2” –num-workers 4
databricks fs cp /local/path/to/python/files dbfs:/mnt/python/files
databricks jobs create –name “my-job” –existing-cluster-id <cluster-id> –python-script dbfs:/mnt/python/files/cli_script.py
databricks jobs run-now –job-id <job-id> –wait
D. databricks clusters create –cluster-name “my-cluster” –instance-profile “my-profile” –num-workers 4
databricks workspace import_dir /local/path/to/python/files /dbfs/mnt/python/files
databricks jobs create –name “my-job” –new-cluster spec-file:/path/to/cluster-spec.json –python-script /dbfs/mnt/python/files/cli_script.py
databricks jobs run-now –job-id <job-id> –wait
Correct Answer: D
Explanation:
Option A is incorrect. This option creates a cluster, imports the Python files into the workspace, and creates a job. It does not, however, outline how to add the Python files to the cluster. Additionally, the script execution will be synchronous when the run-now command is used with the –sync option, which may make it difficult to efficiently capture and save the output to a local file.
Option B is incorrect. This option also creates a cluster and copies the Python files to the DBFS (Databricks File System). It does not, however, use the workspace’s import_dir command, which is a quicker method of uploading files. Additionally, the run-now command does not have the –wait option, which means the script execution might not wait for it to finish, which could cause problems with capturing the output.
Option C is incorrect. This option suffers from the same issues as Option B. The job creation process does not use the import_dir command, and there is no spec-file parameter, which restricts the cluster’s ability to be configured for script execution.
Option D is correct. It covers all the necessary steps and includes the correct commands and options to accomplish the tasks efficiently. It covers all the requirements given in the question as explained in the following:
databricks clusters create: A new cluster with the specified configuration is created by this command. The –cluster-name parameter specifies the cluster’s name, and the –instance-profile parameter configures the cluster’s instance profile. The cluster’s worker nodes are counted using the –num-workers parameter.
databricks workspace import_dir: This command uploads a collection of Python files to the Databricks File System (DBFS) at /dbfs/mnt/python/files from the local path /local/path/to/python/files. This procedure makes sure that the necessary Python files are present in the DBFS and ready for further execution.
databricks jobs create: The “my-job” command creates a brand-new job. The spec-file:/path/to/cluster-spec.json specifies the path to a cluster specification file that defines the cluster configuration, and the –new-cluster parameter specifies that a new cluster should be created for the job. The /dbfs/mnt/python/files/cli_script.py Python script’s location is specified by the –python-script parameter.
databricks jobs run-now: This command starts the job with the specified job-id to run. The command will wait for the job execution to finish before returning if the –wait option is used. This enables recording the script execution output and saving it to a local file or processing the output further.
Reference:
https://docs.databricks.com/dev-tools/cli/index.html
Also Read : Preparation Guide : Databricks Certified Data Engineer Professional Certification
Domain: Databricks Tooling
Question 3. A data engineer is developing an application that needs to programmatically interact with Databricks using its REST API. The data Engineer needs to retrieve the job run details for a specific job and perform further analysis of the obtained data. Which combination of Databricks REST API endpoints should the data engineer use to accomplish this task efficiently?
A. clusters/list and jobs/runs/list
B. jobs/list and jobs/runs/getjobs/runs/get and jobs/list
- jobs/runs/list and clusters/list
Correct Answer: B
Explanation:
To efficiently retrieve the job run details for a specific job and perform further analysis using Databricks REST API, the data engineer should use the combination of jobs/list and jobs/runs/get endpoints.
Option A is incorrect. The list of clusters in a Databricks workspace can be retrieved using the clusters/list endpoint. The details of the job run are not provided, though. Similar to the jobs/runs/list endpoint, the jobs/runs/list endpoint lists all job runs but is not intended for retrieving a specific job run’s details. As a result, Option A is inadequate for completing the task.
Option B is correct. The data engineer can identify the specific job for which they want to retrieve run details by using the jobs/list endpoint, which displays all of the jobs in a Databricks workspace. If you know the job ID, you can use the jobs/runs/get endpoint to retrieve information about that specific job run, including the run ID, status, start time, end time, and more. The data engineer can effectively retrieve the job run details for a particular job using this set of endpoints and go on to further analyze the data that has been retrieved.
Option C is incorrect. The jobs/list endpoint lists every job in a Databricks workspace, whereas the jobs/runs/get endpoint allows retrieving information about a specific job run. It does not specifically give information about job runs. As a result, Option C lacks the set of endpoints needed to effectively retrieve the job run details for a particular job.
Option D is incorrect. The jobs/runs/list endpoint lists all job runs, but it is not designed to allow users to retrieve specific job run information. The job run details cannot be obtained using the clusters/list endpoint, which provides data about clusters in a Databricks workspace. As a result, Option D does not offer the right set of endpoints to effectively complete the task.
Reference:
https://docs.databricks.com/dev-tools/api/index.html#call-the-rest-api
Domain: Databricks Tooling
Question 4. A senior data engineer is working on an extremely intricate and complex data project that necessitates the implementation of a strong and scalable data pipeline using Databricks cutting-edge Delta Lake architecture. The project entails processing enormous amounts of data in real-time, performing complex transformations, and guaranteeing the compatibility and quality of the data. It is essential to create an architecture that makes the most of Delta Lake’s capabilities and offers effective data processing. Which of the following statement would be the most sophisticated architecture for this situation?
A. Employ an advanced data ingestion strategy where the raw data is seamlessly ingested into a Delta Lake table, leveraging the power of schema enforcement and schema evolution. Apply real-time structured streaming to process the data, ensuring the execution of complex transformations, and store the refined results in separate Delta Lake tables. This architecture ensures data integrity, quality, and compatibility throughout the pipeline, providing a solid foundation for advanced data analysis.
B. Opt for an external storage approach where the raw data is stored in widely used cloud storage platforms such as Azure Blob Storage or AWS S3. Harness the robust ACID transactional capabilities offered by Delta Lake to read the data in parallel, perform intricate transformations using the power of Spark SQL, and securely store the processed data back to the external storage. This architecture guarantees the scalability and reliability needed for large-scale data processing.
C. Leverage the innovative Auto Loader feature provided by Delta Lake to automate the seamless loading of data from cloud storage directly into a Delta Lake table. Utilize the power of schema inference to automatically infer the data schema, reducing manual effort. Leverage Delta Lake’s advanced merge capabilities to perform efficient upsert operations and handle any changes or updates in the data. Additionally, leverage the time travel feature of Delta Lake to access previous versions of the data for comprehensive and insightful analysis. This architecture empowers the data engineer to handle dynamic and evolving datasets effectively.
D. Incorporate the MLflow integration feature offered by Delta Lake to streamline the machine learning pipeline within the architecture. Ingest the training data into Delta Lake tables, leveraging the MLflow platform to track experiments, manage model versions, and facilitate seamless collaboration between data scientists and engineers. Leverage the optimized storage and indexing capabilities of Delta Lake to ensure efficient and scalable model serving. This architecture enables the seamless integration of machine learning workflows into the data pipeline, unlocking the full potential of advanced analytics.
Correct Answer: A
Explanation:
Option A is correct. It suggests using a sophisticated data ingestion strategy and making use of Delta Lake’s features to guarantee data integrity, quality, and compatibility through the pipeline by combining a thorough data ingestion strategy with real-time structured streaming for processing and making use of Delta Lake’s features. With the help of schema enforcement and schema evolution, the raw data is seamlessly ingested into a Delta Lake table in this architecture. The data is processed using real-time structured streaming, which carries out complicated transformations and stores the refined outcomes in different Delta Lake tables. This strategy offers a strong basis for sophisticated data analysis, making it the most effective and sophisticated architecture for the situation at hand.
Option B is incorrect. It advises choosing an external storage strategy in which the unprocessed data is kept on well-known cloud storage services like Azure Blob Storage or AWS S3. Although the Delta Lake architecture supports reading data from external storage, this option falls short of utilizing all of its features. It depends on Delta Lake’s ACID transactional capabilities to perform transformations; however, it does not take advantage of Delta Lake’s native integration and advanced features to process data efficiently. As a result, this option is not the best one for the intricate and highly complex data project.
Option C is incorrect. It advises using Delta Lake’s Auto Loader feature to automatically load data from cloud storage into a Delta Lake table. This option also suggests using the time travel function of Delta Lake, merge capabilities, and schema inference. Although these features are useful for managing dynamic and changing datasets, the project’s real-time processing and complex transformations are not covered by this option. Instead of offering a complete solution for effective data processing and analysis, it focuses more on data loading and management. As a result, this option is not the best one for the situation.
Option D is incorrect. It suggests utilizing Delta Lake’s MLflow integration feature to streamline the architecture’s machine learning pipeline. The needs of the complex data project are not directly addressed by MLflow integration, although it can be useful for managing model versions, tracking experiments, and promoting collaboration between data scientists and engineers. Instead of emphasizing the real-time processing, complex transformations, and data quality requirements for the project, this option primarily focuses on machine learning workflows and model serving. As a result, it is not the best option in the given situation.
Reference:
https://delta.io/learn/getting-started
Domain: Databricks Tooling
Question 5. A dataset containing data on sales transactions is given to you as a data engineer employed by Databricks. To create a report that lists the total sales for each product category, you must transform the dataset using PySpark’s DataFrame API. To complete this task effectively, choose the best combination of PySpark DataFrame API operations, including uncommon ones.
Which one of the following codes will be most suitable for the given situation?
A. processedDF = originalDF.groupBy(‘product_category’).agg(expr(‘sum(sales_amount) AS total_sales’)).orderBy(‘total_sales’, ascending=False)
B. processedDF = originalDF.groupBy(‘product_category’).pivot(‘month’).agg(expr(‘sum(sales_amount) AS monthly_sales’)).fillna(0)
C. processedDF = originalDF.groupBy(‘product_category’).agg(expr(‘collect_list(sales_amount) AS sales_list’)).select(‘product_category’, size(‘sales_list’).alias(‘total_sales’))
D. processedDF = originalDF.groupBy(‘product_category’).agg(F.expr(‘summary(“count”, “min”, “max”, “sum”).summary(sales_amount).as(“summary”)’)).select(‘product_category’, ‘summary.sum’)
Correct Answer: B
Explanation:
Option A is incorrect. The suggested method groups the data by the ‘product_category’ column using the groupBy operation, and then computes the total sales for each product category using the agg operation and sum aggregation function. Using the orderBy operation, it also uses a descending order of total sales to sort the result. This option does not, however, involve any unusual functions, and it does not produce the desired result of totaling sales for each product category over various months.
Option B is correct. It is the best option for producing a report that effectively sums up the total sales for each product category. The ‘product_category’ column is used to group the data using the groupBy operation. The DataFrame is then pivoted based on the month column using the uncommon function known as the pivot operation, resulting in distinct columns for the various months. The monthly sales for each product category are determined using the agg operation and sum aggregation function. To ensure that the resulting DataFrame contains all the necessary columns for each product category, the fillna operation is used to replace any null values with 0. This option effectively completes the task at hand by offering a thorough summary of sales for each product category over various months.
Option C is incorrect. The ‘product_category’ column is used to group the data using the groupBy operation. The’sales_amount’ is then gathered into a list for each product category using the agg operation in conjunction with the collect_list aggregation function. The resulting DataFrame chooses the ‘product_category’ column and applies the size function to determine the size of the’sales_list’. However, this option focuses on gathering the sales amounts as a list for each product category rather than taking into account the need to summarize the total sales.
Option D is incorrect. It attempts to use the agg operation while misusing the summary function. To calculate different statistics (count, min, max, sum) for “sales_amount,” it combines the summary function with the sum aggregation. The fact that this option does not use the summary function correctly leads to an incorrect DataFrame. The resultant DataFrame’s selected columns do not give the desired summary of total sales by product category.
Reference:
https://www.databricks.com/blog/2016/02/09/reshaping-data-with-pivot-in-apache-spark.html
Domain: Databricks Tooling
Question 6. A senior data engineer is using Databricks Repos to manage the codebase and communicate with other team members while working on a challenging project. The project entails setting up a data pipeline that is scalable and can handle complex data transformations and analysis. He wants to make use of the Databricks Repos version control features to guarantee code quality and effectiveness. Which of the following options best describes the ideal process for effectively managing code versions in Databricks Repos?
A. Create a new branch for each code change or feature implementation. Once the changes are completed, commit the changes to the branch and merge them into the main branch. Use the tagging feature in Databricks Repos to mark important milestones or releases. Regularly review and merge changes from the main branch to keep the codebase up to date.
B. Keep all code changes in a single branch to maintain a linear commit history. Use descriptive commit messages to track changes. Periodically create snapshots of the entire repository to capture different code versions. Use the snapshot IDs to revert to specific versions if necessary.
C. Create separate branches for development, staging, and production environments. Develop new features and changes in the development branch and regularly merge them into the staging branch for testing. Once the changes are validated, merge them into the production branch for deployment. Use Databricks Repos’ deployment tools to automate the deployment process.
D. Utilize the fork and pull request workflow for code collaboration. Fork the main repository to create a personal copy and make changes in your forked repository. Once the changes are completed, submit a pull request to merge the changes into the main repository. Reviewers can provide feedback and approve the changes before merging them.
Correct Answer: A
Explanation:
Option A is correct. It follows the suggested procedure for efficiently managing code versions in Databricks Repos. For isolated development and simple change tracking, a new branch should be created for every change to the code or addition of a feature. Code integration is maintained by committing the modifications to the branch and merging them into the main branch. Utilizing Databricks Repos’ tagging feature enables marking significant releases or milestones. The main branch’s changes are periodically reviewed and merged, which keeps the codebase current. The senior data engineer can effectively manage code versions, track changes, work with team members, and guarantee code quality and efficiency within the project by adhering to this workflow.
Option B is incorrect. To preserve a straight-line commit history, it advises keeping all code modifications in a single branch. Although having a linear history may initially seem convenient, tracking changes can quickly become difficult, especially when several developers are working on the project. Additionally, taking regular snapshots of the entire repository can lead to redundant storage and make it more difficult to go back to a particular version when necessary. This method falls short of Databricks Repos’ level of control and granularity over code versions.
Option C is incorrect. It advises establishing distinct branches for the development, staging, and production settings. The version control features offered by Databricks Repos are not specifically addressed by this method, even though it is typical in software development. Although environment-specific branches may be a part of the workflow and are relevant to the question’s focus on managing code versions, they do not cover all of the version control features provided by Databricks Repos.
Option D is incorrect. It suggests making use of the fork and pull request workflow, which is frequently employed in scenarios involving collaborative development. Although this workflow encourages code collaboration and review procedures, it does not fully take advantage of the Databricks Repos’ version control features. Version control features outside of Databricks Repos’ control, such as forking the main repository and submitting pull requests are more typically found on websites like GitHub. For efficient code version management, Databricks Repos version control features should be used.
Reference:
https://docs.databricks.com/repos/index.html
Domain: Data Processing
Question 7. Setting up a real-time data pipeline to load data from a streaming source into a Databricks cluster is the responsibility of a data engineer. To give the business insights, the data must be processed quickly as it is being ingested. The engineer chooses to manage the incoming data using Databricks’ Real-Time Workload Autoloader feature. Which of the following steps should the engineer take to configure the Real-Time Workload Autoloader correctly?
A. Create a new table in the Databricks workspace and configure the Real-Time Workload Autoloader to write the data into that table.
B. Create a new directory in the DBFS and configure the Real-Time Workload Autoloader to write the data into that directory.
C. Configure the Real-Time Workload Autoloader to write the data directly into a table in an external database.
D. Configure the Real-Time Workload Autoloader to write the data into an existing table in the Databricks workspace.
Correct Answer: B
Explanation:
Option A is incorrect. The Real-Time Workload Autoloader should be set up to write the data into a new table that has been created in the Databricks workspace. The Autoloader feature, on the other hand, is made specifically to manage streaming data and store it in DBFS. It makes use of Delta Lake’s capabilities, which offer scalability, reliability, and performance for handling streaming data. The advantages of the Autoloader and Delta Lake would not be fully utilized if the data were stored in a table within the Databricks workspace. Therefore, when configuring the Real-Time Workload Autoloader, Option A is not the best option.
Option B is correct. It advises establishing a fresh directory in the DBFS and setting up the Autoloader to write the data into that directory. This strategy is in line with how the Autoloader feature is supposed to be used and how Delta Lake can stream data. The Autoloader can take advantage of Delta Lake’s transactional and optimized storage for quick processing of streaming data by storing the data in the DBFS directory. This guarantees performance, scalability, and reliability when handling high-rate streaming data.
Option C is incorrect. It advises setting up the Autoloader to put the information right into a table in an external database. Databricks can be used to process and ingest data from external databases, but the Real-Time Workload Autoloader feature is intended specifically to handle streaming data inside the Databricks environment. It offers effective streaming data processing and storage capabilities using the DBFS and Delta Lake. As a result, when configuring the Autoloader, Option C is not the best option.
Option D is incorrect. It recommends setting up the Autoloader so that it writes the data into an existing table in the Databricks workspace. For this method to work, a pre-existing table must be available and suitable for storing the streaming data. The Autoloader, on the other hand, uses the capabilities of Delta Lake, which offers optimized storage and processing for streaming workloads, to handle streaming data. The most effective and scalable method might not be writing the data directly into an existing table. Therefore, when configuring the Real-Time Workload Autoloader, Option D is not the best option.
Reference :
Domain: Data Processing
Question 8. A data engineer at a retail company uses Databricks to handle hourly batch jobs while dealing with late-arriving dimensions. The team must find an effective approach to ensure accurate processing within the batch window despite delays in dimension updates. The solution should address complex data relationships, high data volume, real-time analytics requirements, and data consistency. Which option should the team choose?
A. Implement a strict cutoff time for dimension updates, discarding any late arrivals and proceeding with the available data.
B. Extend the batch processing window to accommodate late-arriving dimensions, adjusting the start time as needed.
C.Use an incremental processing approach, handling late-arriving dimensions separately and merging them with the main batch job.
D. Leverage Databricks Delta Lake’s time travel capabilities to capture late-arriving updates and retrieve the latest versions of dimension tables during processing.
Correct Answer: C
Explanation:
Option A is incorrect. This option suggests setting a strict deadline for dimension updates and discarding any updates that arrive after that time. While it might keep the batch window consistent, it disregards the significance of processing all available data. Important data may be lost if late-arriving dimensions are disregarded, which could result in results that are erroneous and incomplete. As a result, the scenario does not lend itself to this option.
Option B is incorrect. To accommodate the inclusion of late-arriving dimensions, this option suggests extending the batch processing window. Extending the processing window ensures that all data is taken into account, but it might not be possible in situations where there are strict time constraints. It may affect the need for real-time analytics and postpone the release of processed data. Furthermore, rather than handling late-arriving dimensions specifically, the approach simply extends the batch window. As a result, this option is not the best one for the circumstances.
Option C is correct. To handle late-arriving dimensions separately and merge them with the main batch job, this option recommends using an incremental processing strategy. Effectively addressing the problem of late-arriving dimensions. Data accuracy and consistency can be preserved by processing them separately before merging them with the main batch job. It enables effective management of large volumes of data and complex data relationships. This choice is the best one because it fits the needs of the scenario as it is presented.
Option D is incorrect. This option suggests capturing late-arriving updates and retrieving the most recent versions of dimension tables during processing by using Databricks Delta Lake’s time travel capabilities. The handling of late-arriving dimensions is not specifically addressed by Delta Lake’s time travel feature, despite the ability to access earlier iterations of tables. It might make the solution more complicated and possibly add extra overhead. As a result, this option is not the best course of action in the given situation.
Reference:
Domain: Data Processing
Question 9. A large healthcare organization’s data engineering team is tasked with using Databricks to construct incrementally processed ETL pipelines. Massive amounts of healthcare data from various sources must be transformed and loaded into a centralized data lake by way of pipelines. The team must overcome several obstacles, including poor data quality, rising data volumes, changing data schema, and constrained processing windows. To guarantee effectiveness and timeliness, the data must be processed in small steps. The team also needs to handle situations where the source data changes or new data is added, as well as guarantee data consistency. Given this situation, which option should the data engineering team choose to effectively build the incrementally processed ETL pipelines?
A. Implement a full refresh strategy, where the entire dataset is processed from scratch during each pipeline run. This approach ensures simplicity and eliminates potential data inconsistencies caused by incremental updates.
B. Use change data capture (CDC) techniques to capture and track changes in the source data. Incorporate the captured changes into the ETL pipeline to process only the modified data. This approach minimizes processing time and resource usage.
C. Employ a streaming approach that continuously ingests and processes the incoming data in real-time. This enables near-instantaneous updates and ensures the pipeline is always up to date with the latest data.
D. Develop a complex event-driven architecture that triggers pipeline runs based on specific data events or conditions. This approach allows for granular control and targeted processing, ensuring optimal performance and minimal processing overhead.
Correct Answer: B
Explanation:
Option A is incorrect. Although it may seem straightforward to implement a full refresh strategy, where the entire dataset is processed from scratch during each pipeline run, it can be very time-consuming and inefficient. Even when there are no significant changes, processing the entire dataset repeatedly wastes resources and extends processing time. It can cause delays in the availability of data because it does not address the need for incremental updates. Additionally, this approach may be impractical and impede prompt processing in scenarios where the data volume is high.
Option B is correct. Using change data capture techniques to capture and track changes in the source data is the most appropriate approach for building incrementally processed ETL pipelines in this complex scenario. CDC allows the team to identify and incorporate only the modified data into the pipeline, minimizing processing time and resource usage. By tracking changes in the source data, the team can efficiently process incremental updates, ensuring efficiency and timeliness. This approach addresses the challenges of data quality issues, evolving data schema, and ever-increasing data volume while maintaining data consistency.
Option C is incorrect. In some use cases, it can be advantageous to use a streaming approach that continuously ingests and processes the incoming data in real-time. It might not, however, be the best option in the circumstances. When real-time or nearly real-time processing is required, streaming approaches are frequently preferable. In this situation, where processing windows are constrained and incremental updates are necessary, a streaming approach might not be appropriate. Furthermore, using a streaming approach adds complexity and infrastructure requirements that might not be required for the task at hand.
Option D is incorrect. Creating a sophisticated event-driven architecture that starts pipeline runs in response to particular data events or conditions can give the processing workflow flexibility and control. However, a complicated event-driven architecture might add needless complexity in this scenario, where the emphasis is on creating ETL pipelines that are incrementally processed. The main goal is to efficiently handle incremental updates while ensuring data accuracy and timeliness. Option D might be more appropriate in situations requiring precise control and targeted processing based on particular events.
Reference:
Domain: Data Processing
Question 10. In a highly regulated healthcare environment, a data engineering team is responsible for optimizing workloads to process and analyze large volumes of patient data using Databricks. The team faces numerous challenges, including strict privacy and security requirements, complex data relationships, and the need for real-time analytics. They must find the most efficient approach to process the data while ensuring compliance, minimizing resource utilization, and maximizing query performance. Additionally, the team needs to handle frequent data updates and provide near real-time insights to support critical decision-making. Which of the following option should the data engineering team choose to successfully optimize their workloads?
A. Utilize Databricks Auto Loader to ingest and process data directly from multiple healthcare data sources. This feature automatically scales resources based on data volume, optimizing performance and reducing processing time. It also provides built-in data validation and error-handling capabilities.
B. Implement a micro-batching approach using Structured Streaming in Databricks. This approach processes data in small, continuous batches, enabling near real-time analytics while minimizing resource consumption. It ensures data consistency and provides fault tolerance in case of failures.
C. Implement a tiered storage approach using Databricks Delta Lake. Store frequently accessed and critical data in high-performance storage tiers while moving less frequently accessed data to cost-effective storage tiers. This strategy optimizes both query performance and storage costs.
D.Implement data partitioning and indexing techniques in Databricks Delta Lake to improve query performance. Partition the data based on relevant attributes, such as patient ID or date, and create appropriate indexes to facilitate faster data retrieval. This approach minimizes the amount of data scanned during queries, resulting in improved performance.
Correct Answer: D
Option A is incorrect. Databricks Auto Loader feature automates the process of ingesting and loading data from different sources. It has built-in data validation capabilities and is scalable. The difficulties of maximizing workloads and query performance in the healthcare environment cannot be directly addressed by Auto Loader, even though it can be useful for automating data ingestion. This option puts more emphasis on data ingestion than workload optimization.
Option B is incorrect. Data processing in small, continuous batches is known as micro-batching. Databricks offers the streaming processing framework known as Structured Streaming. This method allows for almost real-time analytics and offers fault tolerance in the event of errors. Although micro-batching can support near real-time analytics, it might not be the best solution for streamlining workloads and query performance in the medical setting. When compared to other options, processing data in small batches can increase resource consumption and latency, especially when dealing with large volumes of data.
Option C is incorrect. Databricks Delta Lake is a storage layer that enhances the reliability and performance of data lakes. The tiered storage approach involves moving less frequently accessed data to more affordable storage tiers while keeping frequently accessed and critical data in high-performance storage tiers. This tactic can reduce storage expenses while also improving query performance. The challenges of workload optimization and query performance in the healthcare environment may not be directly addressed by tiered storage, despite the fact that it addresses storage optimization. This option places more emphasis on data storage than workload optimization.
Option D is correct. To facilitate quicker data retrieval, this option entails partitioning the data based on pertinent attributes, such as patient ID or date, and creating the necessary indexes. The team can reduce the amount of data that needs to be scanned during queries by partitioning the data and building indexes, which will improve performance and use fewer resources. This method specifically addresses the difficulties associated with maximizing workloads and query performance in the healthcare industry, where there are significant amounts of data and intricate data relationships.
Reference:
https://docs.databricks.com/optimizations/disk-cache.html
Domain: Data Processing
Question 11. In a highly complex and time-sensitive streaming data processing scenario, a data engineering team at a major financial institution is tasked with using Databricks to analyze a sizable amount of real-time financial market data. To support trading decisions, data such as stock prices, trade orders, and market indicators must be processed almost instantly. Among other challenges, the team must manage data spikes during active trading hours, ensure low-latency processing, and maintain data accuracy. They must come up with a workable plan to streamline the pipeline for processing streaming data. To successfully optimize their streaming data processing, which of the following options should the data engineering team choose?
A. Implement window-based aggregations using Databricks Structured Streaming to perform calculations on streaming data within specified time intervals. Use sliding windows or session windows to aggregate and analyze the data with low latency.
B. Utilize Databricks Delta Lake’s streaming capabilities to ingest and process the streaming financial market data. Leverage Delta Lake’s ACID transactions and schema evolution feature to ensure data consistency and handle evolving data structures.
C. Deploy Apache Kafka as the streaming data platform and integrate it with Databricks. Use the Kafka integration to consume the real-time financial market data from Kafka topics and process it efficiently in Databricks.
D. Implement streaming stateful processing using Databricks Structured Streaming. Use the updateStateByKey operation to maintain and update the state of streaming data over time, allowing for complex calculations and analysis of the evolving data.
Correct Answer: D
Explanation:
Option A is incorrect. It suggests using Databricks Structured Streaming to implement window-based aggregations. Window-based aggregations are useful for studying data over specific periods, but they might not be the best option in this case. Low-latency processing is necessary for the financial market data, and window-based aggregations may cause further processing lags. Window-based aggregations might not be adequate to handle updates or changes in the streaming data because the data engineering team also needs to maintain data accuracy.
Option B is incorrect. It suggests utilizing the streaming capabilities of Databricks Delta Lake. Even though Delta Lake offers many features for handling streaming data, including ACID transactions and schema evolution, it might not be the best option in this case. Low-latency processing of the financial market data is necessary, but batch processing is where Delta Lake’s optimizations are more concentrated. Additionally, the management and integration of streaming data may add complications and overhead that could slow down processing and affect performance.
Option C is incorrect. It recommends setting up Apache Kafka as the platform for streaming data and integrating it with Databricks. Kafka is a well-liked option for creating scalable and fault-tolerant streaming pipelines, but it might not be the best choice in this case. Low-latency processing is required for the financial market data, and adding a second streaming platform like Kafka introduces more latency and complexity. To manage the integration between Kafka and Databricks, additional resources and maintenance work may be needed.
Option D is correct. It recommends using Databricks Structured Streaming and the updateStateByKey operation to implement streaming stateful processing. In this case, this option is the best one for stream data processing optimization. Low-latency processing is necessary for the financial market data, and stateful processing enables the team to continuously update and maintain the state of the streaming data. This ensures low-latency processing while enabling complex calculations and analysis of the changing data. In Structured Streaming, the updateStateByKey operation offers a convenient way to carry out incremental updates and maintain the state.
Reference:
https://docs.databricks.com/structured-streaming/stateful-streaming.html
Domain. Data Processing
Question 12. At a large multinational retailer, a senior data engineer is in charge of using Databricks data processing capabilities to build interactive dashboards for examining and visualizing vast amounts of sales and inventory data. The information is divided into several tables with intricate relationships, such as “sales_transactions,” “product_inventory,” and “customer_profiles.” The goal is to deliver intuitive and useful insights to stakeholders through visually appealing dashboards. However, the data engineer faces several difficulties, including the need for real-time analytics and a variety of data sources and data schema. which of the following approach should the data engineer choose to effectively leverage Databricks and create interactive dashboards which will address all the requirements and situations?
A. Use Databricks Delta Lake to store and manage the sales and inventory data. Delta Lake provides transactional capabilities and schema enforcement, ensuring data consistency and reliability. Leverage Delta Lake’s time travel feature to create snapshots of the data at different points in time, enabling historical analysis in the dashboards.
B. Develop interactive dashboards using Databricks notebooks with visualization libraries such as Matplotlib or Plotly. Use PySpark to perform data transformations and aggregations, and generate visualizations directly within the notebook. Embed the notebooks into a Databricks workspace for easy access and collaboration.
C. Integrate Databricks with a business intelligence (BI) tool like Tableau or Power BI. Connect Databricks as a data source in the BI tool and create visually stunning dashboards using the tool’s drag-and-drop interface and rich visualization options. Leverage Databricks’ scalable data processing capabilities to ensure real-time data updates in the dashboards.
D. Utilize Databricks SQL Analytics to create interactive dashboards. Write SQL queries to aggregate and analyze the sales and inventory data, and use Databricks’ built-in visualization capabilities to generate interactive charts and graphs. Publish the dashboards to the Databricks workspace for easy sharing and collaboration.
Correct Answer: C
Explanation:
Option A is incorrect. It suggests utilizing the transactional capabilities and schema enforcement of Databricks Delta Lake to store and manage the sales and inventory data. Although the time travel function in Delta Lake enables historical analysis, it does not directly address the need for developing interactive dashboards. In contrast to dashboard visualization, Delta Lake places a greater emphasis on data management and reliability.
Option B is incorrect. It suggests using Databricks notebooks and visualization tools like Matplotlib or Plotly to create interactive dashboards. While PySpark and notebooks offer flexibility in data transformations and aggregations, the interactivity and flexibility needed for interactive dashboards may be constrained by the visualizations created within the notebook. Additionally, integrating notebooks into a Databricks workspace might not provide the same level of sharing and collaboration features as specific dashboard tools.
Option C is correct. It recommends connecting Databricks to a business intelligence (BI) tool like Tableau or Power BI. The data engineer can use the BI tool’s drag-and-drop interface and wealth of visualization options to create visually stunning dashboards by connecting Databricks as a data source. This strategy makes use of Databricks’ scalable data processing capabilities and enables the real-time data updates needed in the dashboards. This option offers a complete solution for developing interactive dashboards that satisfy the scenario’s requirements by combining the strength of Databricks and BI tools.
Option D is incorrect. It suggests generating interactive dashboards with Databricks SQL Analytics. While the sales and inventory data can be combined and analyzed using SQL queries, the built-in visualization features of Databricks might not be as flexible and interactive as specialized dashboard tools. Sharing and collaboration are made possible by publishing the dashboards to the Databricks workspace, but some sophisticated features offered by specialized BI tools might not be present.
Reference:
https://docs.databricks.com/partners/bi/power-bi.html
https://www.tableau.com/solutions/databricks
Domain: Data Processing
Question 13. A multinational e-commerce company is using Databricks for processing and analyzing sales data. The data engineering team must put in place a solution to deal with alterations in customer addresses over time while keeping track of address updates historically. The team must manage a sizable customer base, deal with frequent address changes, and ensure data accuracy for reporting purposes, among other challenges. Which approach should the team choose to effectively manage the changes in customer addresses in a scalable and efficient manner?
Option.
A. Implement SCD Type 1 by updating the customer dimension table with the latest address information.
B. Implement SCD Type 2 by creating a new row in the customer dimension table for each address change.
C. Implement SCD Type 3 by adding columns to the customer dimension table to store previous address values and update the current address column with the latest information.
D. Implement SCD Type 4 by creating separate dimension tables to track address changes and updating the main customer dimension table with the latest address information.
Correct Answer: B
Explanation:
Option A is incorrect. The SCD Type 1 approach involves overwriting the current values in the customer dimension table and updating it with the most recent address data. Although this method may be easy to understand and straightforward, the historical record of address changes is not preserved. Analyzing historical trends or following address changes over time is challenging because once the address is updated, the previous address information is lost. The requirement to keep a historical record of address updates means that this option is not appropriate.
Option B is correct. The best approach for managing changes in customer addresses while maintaining a historical record is the SCD Type 2 approach. For each address change, it entails adding a new row to the customer dimension table while retaining the previous and earlier address data. This method offers a complete history of customer addresses for reporting and analysis purposes and enables accurate tracking of address changes over time. It guarantees data accuracy and makes it possible to analyze address trends, customer movements, and metrics unique to addresses. By maintaining a separate row for each address change, it allows for easy retrieval of historical address information without impacting the integrity of the customer dimension table. Therefore, SCD Type 2 is the correct option for effectively managing the changes in customer addresses in a scalable and efficient manner.
Option C is incorrect. Using the SCD Type 3 approach, new columns are added to the customer dimension table to store previous address values, and the current address column is updated with the most recent data. This method makes it possible to store a small amount of historical address data, but managing and tracking multiple address changes over time becomes difficult. Each attribute change also necessitates changing the table structure by adding new columns, which can result in a rise in complexity and storage needs. This option might not be the most effective or scalable method for dealing with frequent address updates as a result.
Option D is incorrect. In the SCD Type 4 approach, distinct dimension tables are made to keep track of address changes, and the primary customer dimension table is updated with the most recent address data. This strategy enables the tracking of address changes separately and the preservation of historical records, but it also adds complexity by requiring the maintenance of multiple tables and the management of data consistency between them. To get the most recent address information, it might also be necessary to run additional joins or queries, which could slow down the query execution. As a result, this option might not be the simplest or most effective way to manage address changes on a scalable basis.
Reference:
Domain: Data Processing
Question 14. A financial institution’s data engineering team is in charge of streamlining workloads to process and examine enormous amounts of transaction data using Databricks. The team faces difficulties managing data skew, minimizing data shuffling, and enhancing general job performance. To reduce workloads and ensure effective data processing, they must determine the best strategy. Which option should the data engineering team select in this scenario to successfully optimize their workloads?
A.df.repartition(“transaction_date”).sortWithinPartitions(“transaction_id”).write.parquet(“/optimized/transaction_data”)
B. df.coalesce(1).write.parquet(“/optimized/transaction_data”)
C. df.withColumn(“transaction_year”, year(“transaction_date”)).groupBy(“transaction_year”).count()
D. df.sample(fraction=0.1, seed=42)
Correct Answer: B
Explanation:
Option A is incorrect. To use this option, the data must first be reorganized based on the “transaction_date” column and sorted by “transaction_id” before being written to the Parquet format. Repartitioning and sorting can be helpful in some situations, but this method may not be able to fully address the issues raised in the question, such as reducing data shuffle and dealing with data skew. It’s also possible that workload optimization doesn’t require sorting within partitions. As a result, option A is not the best one for this circumstance.
Option B is correct. This option entails reducing the number of partitions to 1 using the coalesce function before writing the data to the Parquet format. It can enhance overall job performance by reducing data shuffling by combining the data into a single partition. When dealing with small datasets or writing the data to a single output file, this strategy is especially useful. As a result, option B is the best option for this situation’s workload optimization.
Option C is incorrect. This option involves categorizing the data by “transaction_year” to perform a count and adding a new column “transaction_year” based on the “transaction_date” column. The challenges of workload optimization, handling data skew, and minimizing data shuffling are not directly addressed by this operation, even though they may be useful for data analysis based on transaction years.
Option D is incorrect. This option represents sampling the data with a seed and a fraction of 0.1 (10% of the data). The challenges of workload optimization and enhancing overall job performance may not be directly addressed by data sampling, even though they can be useful for exploratory analysis or testing. Sampling the data might not give a true picture of the entire set of data and might not be able to handle data skew or lessen data shuffling.
Reference:
Domain: Data Processing
Question 15. A data engineer is working on a complex data processing project using Databricks and wants to leverage the AutoLoader feature to load JSON files stored in cloud storage into Python DataFrames. The JSON files have nested fields and arrays that are organized hierarchically. Before continuing with the processing, the engineer must perform specific transformations on the loaded data. Which syntax for a Python DataFrame should the engineer use to load the JSON files, automatically infer the schema, and perform the necessary transformations?
A. df = spark.readStream.format(“cloudfiles”).option(“format”, “json”).option(“inferSchema”, “true”).load(“dbfs:/mnt/data”)
df = df.select(“field1”, “field2”, explode(“field3”).alias(“nested_field”))
B. df = spark.read.format(“json”).option(“inferSchema”, “true”).load(“dbfs:/mnt/data”)
df = df.select(“field1”, “field2”, explode(“field3”).alias(“nested_field”))
C. df = spark.readStream.format(“autoloader”).option(“format”, “json”).option(“inferSchema”, “true”).load(“dbfs:/mnt/data”)
df = df.select(“field1”, “field2”, explode(“field3”).alias(“nested_field”))
D. df = spark.read.format(“cloudfiles”).option(“format”, “json”).option(“inferSchema”, “true”).load(“dbfs:/mnt/data”)
df = df.select(“field1”, “field2”, explode(“field3”).alias(“nested_field”))
Correct Answer: A
Explanation:
Option A is correct. It correctly makes use of the AutoLoader feature, which is mentioned in the question, by using the syntax spark.readStream.format(“cloudfiles”). The input files are in JSON format because the format is set to “json” using the syntax option(“format”, “json”). The JSON data is used to automatically infer the schema using the option option(“inferSchema”, “true”). To transform the DataFrame, select the desired fields, then use explode(“field3”).To handle nested arrays, use alias(“nested_field”).
Option B is incorrect. The AutoLoader feature is not used, even though it correctly reads JSON files using the spark.read.format(“json”) syntax and infers the schema using option(“inferSchema”, “true”). There is no mention of streaming or real-time data processing, and the format specified is “json” rather than “cloudfiles” or “autoloader”.
Option C is incorrect. It correctly uses the syntax spark.readStream.format(“autoloader”) to enable the AutoLoader feature, but it also uses option(“format”, “json”) to specify the format when it should have used “autoloader” as the format. The rest of the syntax and transformations are accurate, but this option is incorrect because it is unnecessary.
Option D is incorrect. Similar to Option C, it uses spark.read.format(“cloudfiles”) to specify the format, which is incorrect for the AutoLoader feature. The proper format is “autoloader”. The rest of the syntax and transformations are accurate, but this option is incorrect due to the incorrect format choice.
Reference:
https://docs.databricks.com/ingestion/auto-loader/options.html#json-options
Domain: Data Modeling
Question 16. A data engineer is working on a real-time data analytics project where she needs to ingest streaming data from multiple sources into Databricks using Kafka. To perform real-time analysis to identify popular products based on the number of views within a sliding window of 10 minutes, the data includes user activity logs from an e-commerce platform. Additionally, she also needs to store the outcomes in a different Kafka topic for later processing. Which of the following code snippets correctly implements the required functionality?
A. input_df.selectExpr(“CAST(value AS STRING)”) \
.groupBy(window(“timestamp_column”, “10 minutes”), “product_id_column”) \
.count() \
.writeStream \
.format(“kafka”) \
.option(“kafka.bootstrap.servers”, “<kafka_bootstrap_servers>”) \
.option(“topic”, “<output_kafka_topic>”) \
.start() \
.awaitTermination()
B. input_df.selectExpr(“CAST(value AS STRING)”) \
.groupBy(window(“timestamp_column”, “10 minutes”), “product_id_column”) \
.count() \
.writeStream \
.format(“console”) \
.start() \
.awaitTermination()
C. input_df.selectExpr(“CAST(value AS STRING)”) \
.groupBy(window(“timestamp_column”, “10 minutes”), “product_id_column”) \
.count() \
.select(“window.start”, “window.end”, “product_id_column”, “count”) \
.writeStream \
.format(“kafka”) \
.option(“kafka.bootstrap.servers”, “<kafka_bootstrap_servers>”) \
.option(“topic”, “<output_kafka_topic>”) \
.start() \
.awaitTermination()
D. input_df.selectExpr(“CAST(value AS STRING)”) \
.groupBy(window(“timestamp_column”, “10 minutes”), “product_id_column”) \
.count() \
.writeStream \
.format(“kafka”) \
.option(“kafka.bootstrap.servers”, “<kafka_bootstrap_servers>”) \
.option(“topic”, “<output_kafka_topic>”) \
.start() \
.awaitTermination()
Correct Answer: A
Explanation:
Option A is correct. The code snippet in Option A utilizes input_df to successfully read the streaming data from Kafka. The required transformations are then carried out, such as selecting the value column as a string, grouping the data using window(“timestamp_column”, “10 minutes”), and calculating the number of times each product ID occurs within the window. Finally, it writes the result to a separate Kafka topic specified by <output_kafka_topic>. The awaitTermination() function allows the program to end gracefully. The requirements in the question are correctly implemented by this option. It counts the number of each product ID within the window after applying the necessary transformations to the streaming data, such as the sliding window and grouping. The outcomes are next written to an additional Kafka topic for processing.
Option B is incorrect. The code snippet for this option deviates from the specifications because it uses format(“console”) to write the results to the console rather than putting them in a separate Kafka topic. The output is printed to the console rather than being stored for later processing, even though it makes all the necessary transformations, such as the sliding window and grouping.
Option C is incorrect. Regarding the transformations used, the code snippet in this option is similar to Option A. The count of occurrences for each product ID within the sliding window is calculated, and groupBy is used correctly with the sliding window. However, it also includes the additional step of using select(“window.start”, “window.end”, “product_id_column”, “count”) to choose particular columns from the resulting DataFrame. Since the window, product ID, and count columns are already included by default in the result DataFrame, this step is not required. Therefore, choosing specific columns in this situation would be redundant and unnecessary complexity.
Option D is incorrect. This option’s code snippet has the same problem as Option B. It accurately performs the required transformations, calculates the number of occurrences within the sliding window, but also goes through the extra step of choosing particular columns. This process increases complexity without adding any value. Additionally, this option does not fulfill the requirement to store the results in a separate Kafka topic.
Reference:
Domain: Data Modeling
Question 17. In a large-scale data processing project, A data architect is tasked with designing a data architecture using Databricks for a company that operates globally. Massive data volumes must be handled by the architecture, which must also support real-time analytics and offer high availability. The architect decides to implement a Silver and Gold architecture on Databricks after giving it some thought. While the Gold layer concentrates on cutting-edge analytics and reporting, the Silver layer handles data ingestion, cleansing, and simple transformations. However, because of the project’s complexity, you run into a tricky circumstance that calls for careful thought and knowledge. Which of the following scenarios best fits the current situation in the context of Silver and Gold architecture?
A. The data engineering team notices a significant increase in data ingestion rates, causing a bottleneck at the Silver layer. To handle the increased load, they decide to horizontally scale the Silver layer by adding more worker nodes to the Databricks cluster. This approach helps distribute the incoming data across multiple nodes, improving performance and reducing ingestion latency.
B. The analytics team requires real-time insights from the data stored in the Gold layer. However, the current architecture’s design restricts real-time data processing capabilities. To address this, the team decides to implement change data capture (CDC) mechanisms to capture and replicate data changes in real-time from the Silver layer to the Gold layer. This ensures that the analytics team has access to the most up-to-date data for real-time analysis.
C. The company wants to minimize data duplication and optimize storage costs in the Silver and Gold layers. To achieve this, the team considers implementing data lake optimization techniques, such as delta optimization and data skipping. These techniques allow for efficient storage and query performance by leveraging data indexing, compaction, and caching mechanisms.
D. The Gold layer consists of multiple analytical models and workflows that require iterative development and testing. However, the current setup lacks an efficient way to manage and version the models. To address this challenge, the team decides to leverage MLflow, an open-source platform for managing the machine learning lifecycle. MLflow provides versioning, experiment tracking, and model deployment capabilities, allowing the team to streamline the development and deployment process in the Gold layer.
Correct Answer: B
Explanation:
Option A is incorrect. To handle the increased data ingestion rates, the scenario suggests adding more worker nodes to the Databricks cluster and horizontally scaling the Silver layer. While this strategy can aid in data distribution and performance enhancement, it does not directly address the need for real-time analytics in the Gold layer. Scaling the Silver layer mainly concentrates on data ingestion and does not improve the Gold layer’s real-time data processing capabilities.
Option B is correct. In the context of the Silver and Gold architecture, it is the most appropriate scenario to handle the difficult situation. Real-time insights from the data stored in the Gold layer are specifically addressed in the scenario in Option B. Data changes from the Silver layer can be captured and replicated in real-time to the Gold layer by implementing change data capture (CDC) mechanisms. By doing this, it is made sure that the analytics team has access to the most recent data for analysis in real-time. The requirements outlined in the scenario are aligned with CDC’s real-time data processing capabilities.
Option C is incorrect. The scenario in this option discusses the use of data lake optimization techniques like delta optimization and data skipping to reduce data duplication and optimize storage costs in the Silver and Gold layers. The need for real-time analytics is not specifically addressed by these techniques, although they are useful for efficient storage and query performance. While concentrating on increasing storage and query effectiveness, data lake optimization techniques do not directly support real-time data processing capabilities.
Option D is incorrect. It deals with the problem of managing and versioning workflows and analytical models in the Gold layer. The development and deployment process is suggested to be streamlined by using MLflow, an open-source platform for managing the machine learning lifecycle. The need for real-time data processing capabilities is not directly addressed by this, although it is a valid consideration. It is not essential in this situation, but MLflow primarily focuses on managing machine learning models and experiments.
Reference:
Domain: Data Modeling
Question 18. In a data processing project, a large dataset is stored in Databricks Delta Lake. The dataset represents global sales transactions for an e-commerce platform, containing millions of records. To optimize query performance and facilitate efficient data retrieval, which partitioning key best optimizes query performance for efficient data retrieval?
A. Partitioning the data by the “country” column for country-specific analysis.
B. Partitioning the data by the “year” column for time-based analysis.
C. Partitioning the data by the “product_category” column for category-specific analysis.
D. Partitioning the data by the “store_id” column for store-level analysis.
Correct Answer: B
Explanation:
Option A is incorrect. When it is necessary to filter data based on particular countries, partitioning the data by the “country” column can be useful. Partitioning by country may not be the best option in the given scenario, where the objective is to optimize query performance and enable effective data retrieval. Partitioning solely by country may not produce effective data retrieval for other types of queries and does not directly address the need for time-based analysis.
Option B is correct. By partitioning the data by the “year” column, the architecture can effectively optimize query performance and enable effective data retrieval. Partitioning by year is crucial for time-based analysis because it enables efficient filtering and aggregation based on specific years. For queries that require data aggregation over various periods or time ranges, this partitioning strategy can be very helpful. It accelerates and sharpens data retrieval, improving the project’s overall data processing performance.
Option C is incorrect. When it’s necessary to filter data based on particular product categories, it can be helpful to partition the data by the “product_category” column. Similar to Option A, this partitioning strategy does not, however, directly address the need to improve query performance and make it easier to retrieve data efficiently according to time. It might not be the best option in the given situation.
Option D is incorrect. When store-level analysis and store-specific data filtering are required, partitioning the data by the “store_id” column can be beneficial. Similar to the earlier choices, this partitioning strategy does not, however, directly address the demand for an effective time-based analysis. For time-based queries, it might not lead to the best query performance and most effective data retrieval.
Reference:
https://docs.databricks.com/tables/partitions.html#when-to-partition-tables-on-databricks
Domain: Data Modeling
Question 19. A team of data scientists using Spark DataFrames is working on a challenging situation involving cloning operations as part of a complex data engineering project using Databricks. To ensure effective memory usage, top performance, and precise results when working with large datasets, the team must choose the right clone strategy. The team must carefully weigh the benefits and drawbacks of each clone strategy to navigate this complex situation. Which of the following clone strategies is the best option for the team, given the circumstances?
A. Perform a deep clone operation on the Spark DataFrames to create separate copies of the data. This approach ensures data isolation and prevents any unintended modifications to the original DataFrame. However, deep cloning can consume significant memory resources, especially for large datasets, and may impact performance. It provides a high level of data integrity but at the cost of increased memory usage.
B. Use a shallow clone operation on the Spark DataFrames to create lightweight references to the original data. This approach minimizes memory usage as it does not create separate copies of the data. However, care must be taken when modifying the cloned DataFrame, as any changes made will also affect the original DataFrame. Shallow cloning offers memory efficiency but requires cautious handling to prevent unintended side effects.
C. Combine both deep clone and shallow clone operations based on specific DataFrame partitions. Perform a deep clone on partitions where modifications are expected, ensuring data isolation and accuracy. Use shallow clones for partitions where read-only operations are performed to optimize memory usage and performance. This approach offers a balance between data isolation and memory efficiency but requires careful partitioning and management. It leverages the benefits of both deep and shallow cloning, adapting to different use cases within the data processing project.
D. Implement a custom clone strategy using advanced memory management techniques, such as Apache Arrow or Off-Heap Memory. This approach allows for fine-grained control over memory utilization and performance. However, it requires extensive knowledge and expertise in memory management techniques, making it a more complex solution to implement. Custom clone strategies can provide tailored optimizations but at the cost of additional complexity and maintenance.
Correct Answer: C
Explanation:
Option A is incorrect. To ensure data isolation and avoid unintended modifications, it advises performing a deep clone operation on the Spark DataFrame to produce separate copies of the data. Deep cloning ensures data integrity, but it can use a lot of memory, especially for large datasets. This method is less suitable for situations where memory efficiency is a key consideration because it may result in memory constraints and potential performance degradation.
Option B is incorrect. The Spark DataFrame is to be subjected to a shallow clone operation that would produce lightweight references to the original data. By avoiding the creation of separate copies, shallow cloning reduces the amount of memory used. The original DataFrame will also be affected by changes made to the cloned DataFrame, raising the possibility of unintended consequences and possible data inconsistencies. The need for careful handling and awareness of shared references may make the logic of the code more complex and make it more difficult to maintain data integrity.
Option C is correct. It suggests a combined strategy based on particular DataFrame partitions that combine deep cloning and shallow cloning. With this approach, performance optimization, memory efficiency, and data isolation are all balanced. The team can produce accurate results while maximizing memory usage and upholding acceptable performance levels by performing a deep clone on partitions where modifications are anticipated and using shallow clones for read-only partitions. To fully utilize the advantages of both clone strategies, careful data management and partitioning are necessary. This method enables the team to make effective use of memory resources while maintaining data integrity by allowing it to be flexible and adaptable to various use cases within the data processing project.
Option D is incorrect. It recommends using cutting-edge memory management strategies like Apache Arrow or Off-Heap Memory to implement a unique clone strategy. While offering fine-grained control over memory usage and performance, such techniques call for in-depth knowledge and proficiency in memory management. Implementing custom clone strategies requires a great deal of skill because they run the risk of adding layers of code complexity, increasing maintenance requirements, and creating compatibility problems.
Reference:
Domain: Data Modeling
Question 20. A data architect is faced with a challenging situation in a project using Databricks. The task at hand entails creating a data model for a sizable e-commerce site that sells a wide range of goods. To enhance the data model and facilitate effective data retrieval, the data architect chooses to employ a lookup table approach. But because of the project’s complexity, the architect runs into a singular and challenging situation that necessitates a calculated response. The e-commerce site keeps a sizable inventory of goods, including clothes, electronics, home appliances, and more.
Every product category has a unique set of qualities and traits. The platform also provides several services, including customer reviews, ratings, and recommendations that are linked to particular products.
The difficulty arises from the requirement to quickly query and retrieve product data, along with the attributes and services that go along with it. The data architect is aware that the variety of products and services might make a traditional relational database schema produce a complex and ineffective data model.
The data architect chooses to use a lookup table approach to address this problem. The goal is to develop a central lookup table that houses the characteristics and offerings for each class of products. The lookup table will act as a guide to help users quickly find the details they require for any given product.
The lookup table must support a variety of product categories, each with its own set of characteristics and offerings. The lookup table’s performance and scalability must also be taken into account by the data architect as the e-commerce platform develops and over time adds new product categories.
In this situation, which of the following statement presents the most effective solution by addressing the data architect’s requirements?
A. The data architect decides to create a single lookup table that includes all the attributes and services for all product categories. This approach aims to centralize the data and simplify the querying process by having a unified structure. The architect implements advanced indexing techniques and optimizations to ensure efficient data retrieval.
B. The data architect chooses to create separate lookup tables for each product category, specifically tailored to their unique attributes and services. This approach allows for a more granular and specialized data model, enabling optimized querying and retrieval. The architect implements a dynamic schema design that adapts to the evolving product categories.
C. The data architect opts for a hybrid approach by creating a combination of a centralized lookup table for common attributes and individual lookup tables for specific product categories. This approach strikes a balance between centralization and specialization, providing efficient querying for common attributes while allowing flexibility for category-specific attributes and services.
D. The data architect decides to leverage the power of Databricks Delta Lake’s schema evolution capabilities. Instead of using a traditional lookup table, the architect employs a nested data structure, where each product category is represented as a nested object with its attributes and services. This approach allows for a flexible and scalable data model, accommodating new product categories seamlessly.
Correct Answer: C
Explanation:
Option A is incorrect. It might seem centralized and straightforward to create a single lookup table for all product categories. The need for specialized attributes and services for each product category isn’t addressed, though. This strategy would produce a large and ineffective data model, making it difficult to optimize retrieval and querying. Performance may be enhanced by using advanced indexing techniques, but the need for category-specific attributes would not be met.
Option B is incorrect. It might seem that constructing separate lookup tables for each class of product would offer a detailed and specialized data model. However, this method makes managing multiple tables and their relationships more difficult. As new product categories are added, maintaining and updating the schema can be difficult. Performance may also be affected by querying and joining data from various tables. A dynamic schema design may produce a fragmented and less effective data model even though it can accommodate changing product categories.
Option C is correct. Combining a centralized lookup table for common attributes with separate lookup tables for distinct product categories to create a hybrid approach strikes a balance between centralization and specialization. This strategy offers flexibility for category-specific attributes and services while enabling efficient querying of common attributes. It makes retrieval more efficient and removes the hassle of managing multiple tables. A schema that meets the various requirements of various product categories while maintaining performance and scalability can be created by the data architect. The requirements are effectively met by Option C.
Option D is incorrect. An original strategy is to use a nested data structure and Databricks Delta Lake’s schema evolution capabilities. However, it might add complexity and difficulties to the data management and querying processes. Working with nested data structures can be difficult, especially when handling intricate queries and aggregations. For data retrieval, it might not offer the desired performance and efficiency. Although Delta Lake’s schema evolution capabilities are strong, they might not be the best choice in this specific situation.
Reference:
https://docs.databricks.com/dev-tools/api/python/latest/feature-store/entities/feature_lookup.html
Domain: Data Modeling
Question 21. A large e-commerce company’s data engineering team is faced with a difficult situation when managing and updating a customer-facing table using Databricks in a production environment. Millions of users receive crucial information from the customer-facing table, such as product specifications, costs, and stock levels.
A constant stream of new products, price adjustments, and inventory updates are added to the table. The team must modify the data and table’s structure without impairing user experience or introducing inconsistencies. The need to maintain data integrity and make sure the changes are implemented precisely and effectively makes the situation even more difficult.
The team must come up with a plan that reduces downtime, prevents data inconsistencies, and upholds high performance. The task is made more difficult by the short deadline for putting the changes into effect. A rollback strategy must be developed by the team in case there are any problems during the process.
Which of the following strategies should the data engineering team adopt in light of this situation to efficiently manage and modify the customer-facing table?
A. The data engineering team decides to take an offline approach to manage and change the customer-facing table. They plan to take a maintenance window during off-peak hours to halt all user operations temporarily. During this window, they will make the necessary changes to the table’s structure and data. They will also apply any required data transformations and validations to ensure consistency. Once the changes are successfully applied, they will resume user operations.
B. The data engineering team opts for an online approach using Databricks Delta Lake’s ACID transactions and schema evolution capabilities. They plan to leverage the transactional capabilities of Delta Lake to perform atomic and consistent changes to the customer-facing table. They will use the schema evolution feature to modify the table’s structure and apply the necessary data transformations.
C. The data engineering team decides to create a temporary table to hold the modified structure and data. They plan to perform all the necessary changes and transformations on the temporary table while keeping the original customer-facing table intact. Once the changes are successfully applied to the temporary table and validated, they will swap the temporary table with the original table using an atomic operation. This approach allows the team to minimize downtime by performing the changes offline and only swapping the tables at the last step.
D. The data engineering team chooses to implement a gradual rollout strategy to manage and change the customer-facing table. They plan to introduce the changes incrementally to a subset of users while monitoring the impact and collecting feedback. This approach allows them to assess the changes’ effectiveness, identify any issues, and make adjustments if needed. Once the changes have been thoroughly tested and validated, they will gradually roll them out to the entire user base.
Correct Answer: B
Explanation:
Option A is incorrect. The offline strategy involves setting up a maintenance window for the customer-facing table during off-peak hours. Although this strategy guarantees a specific window of time for modifications, it runs the risk of impairing user operations and resulting in downtime. It might not be appropriate for a significant e-commerce platform where continuous availability is essential. Resuming user operations after the changes can also be difficult, particularly if the procedure takes longer than expected.
Option B is correct. The data engineering team should choose an online strategy utilizing the ACID transactions and schema evolution capabilities of Databricks Delta Lake. This strategy makes use of Delta Lake’s transactional capabilities to guarantee atomic and reliable changes to the table that customers see. The team can alter the table’s structure and carry out the required data transformations while preserving data integrity and averting inconsistencies by using schema evolution. This method minimizes downtime and guarantees a seamless user experience by allowing changes to be made while the table is still in use.
Option C is incorrect. A wise strategy to reduce downtime is to create a temporary table and make changes to it while keeping the original table unaltered. However, if not done properly, switching the tables using an atomic operation can still pose risks and result in possible data inconsistencies. Ensuring a seamless transition between the temporary and original tables complicates the process and might involve extra work.
Option D is incorrect. Before rolling out changes to all users, the gradual rollout strategy enables testing and feedback gathering. Although this method guarantees thorough validation, it might not be appropriate in circumstances involving short deadlines and the need for urgent changes. Multiple versions of the customer-facing table must be carefully monitored and managed to avoid complexity and potential inconsistencies.
Reference:
https://www.databricks.com/glossary/acid-transactions
https://www.databricks.com/blog/2019/09/24/diving-into-delta-lake-schema-enforcement-evolution.html
Domain: Security and Governance
Question 22. Building a production pipeline for handling sensitive financial data is necessary for a financial organization, and it must also ensure that the data is securely deleted as per GDPR and CCPA regulations. The lead data engineer must create a safe and effective pipeline that complies with compliance standards after deciding to use Databricks for this purpose. Which of the following strategy should the data engineer employ to guarantee that data is securely deleted?
A. Use Databricks Delta and enable the time travel feature, then periodically clean up all versions of the data that are older than the allowed retention period.
B. Use the Databricks DBUtils.fs.rm() function to delete the data files directly from the storage layer.
C. Use a combination of encryption and obfuscation techniques to render the sensitive data useless, and then delete the encrypted data using a secure delete utility.
D. Use Databricks Delta to create a garbage collection process that periodically scans and removes orphaned files that are no longer needed.
Correct Answer : D
Explanation:
Option A is incorrect. It recommends utilizing Databricks Delta and turning on the time travel function, after which it is advised that all versions of the data that are older than the permitted retention period be routinely cleaned up. For data management and auditing purposes, using Databricks Delta and managing data versions is a good practice, but it does not specifically address the secure deletion of sensitive financial data. Removing outdated versions based solely on the retention period does not ensure that the information is safely erased from the system. A more forceful and explicit approach to secure deletion is required by compliance regulations.
Option B is incorrect. It advises deleting the data files directly from the storage layer using the Databricks DBUtils.fs.rm() function. Although this function enables the deletion of data files, it is insufficiently secure and compliant to safely erase sensitive financial data. To comply with the stringent requirements of GDPR and CCPA, deleting files at the storage layer might not be sufficient. Retention periods, secure deletion methods, or compliance requirements are not taken into account.
Option C is incorrect. It suggests making the sensitive data useless by using a combination of obfuscation and encryption methods and then erasing the encrypted data using a secure delete utility. The secure deletion requirements of the GDPR and CCPA are not specifically addressed by encryption and obfuscation techniques, even though they can protect sensitive data. Although they render the data unintelligible or meaningless, encryption and obfuscation do not ensure secure deletion. Specific procedures for securely deleting data that make sure it can’t be recovered or recreated are required by compliance regulations. The compliance requirements might not be satisfied by merely using a secure delete utility to remove the encrypted data.
Option D is correct. It suggests developing a garbage collection process with Databricks Delta that periodically scans and deletes orphaned files that are no longer required. This strategy complies with GDPR and CCPA requirements for securely erasing sensitive financial data. Orphaned files are found and removed during the garbage collection process, lowering the possibility of data exposure. The data engineer can implement a safe and effective pipeline for data deletion by utilizing the capabilities of Databricks Delta.
Reference:
Domain: Security and Governance
Question 23. A financial services company that manages sensitive customer data, including Personally Identifiable Information (PII), employs a data engineer. To process and analyze this data while upholding the highest standards of data security and adhering to privacy laws, the company is currently developing a production pipeline. The data engineer is in charge of creating a reliable and secure production pipeline that effectively manages PII.
Which of the following architectural decisions and practices would offer the most thorough protection for PII when creating a production pipeline for sensitive financial data?
A. Employ a tokenization approach where PII is replaced with unique tokens that have no meaningful relationship to the original data. Utilize a secure tokenization service to generate and manage tokens. Implement strict access controls and audit logs to track token usage. Regularly rotate and refresh tokens to mitigate the risk of data breaches.
B. Use data anonymization techniques to replace sensitive PII with randomized or hashed values. Implement a separate data anonymization pipeline that processes the PII before it enters the production pipeline. Ensure that only approved personnel have access to the mapping between anonymized and original data. Monitor and restrict access to the anonymized data to further protect PII.
C. Implement end-to-end encryption throughout the entire pipeline, including data at rest and in transit. Utilize secure key management systems and encryption algorithms to protect PII from unauthorized access. Implement strict access controls and monitoring mechanisms to track and audit data access. Regularly conduct security audits and penetration testing to identify vulnerabilities and ensure compliance with privacy regulations.
D. Implement data redaction techniques to selectively remove or mask sensitive PII in the production pipeline. Utilize advanced masking algorithms to ensure that redacted data is irreversible and cannot be reconstructed. Implement robust access controls and encryption mechanisms to protect both original and redacted data. Regularly monitor and review the redaction process to ensure accuracy and compliance.
Correct Answer: C
Explanation:
Option A is incorrect. It recommends using tokenization to replace private information with special tokens. While tokenization can add another layer of security, end-to-end encryption may offer a higher level of security. The security of the tokenization service and the sturdiness of the access controls are prerequisites for tokenization. The original PII may be subject to unauthorized access if the tokenization service is compromised. Additionally, data in transit and at rest are not encrypted by tokenization, making them susceptible to security breaches.
Option B is incorrect. It advises replacing PII with random or hashed values using data anonymization techniques. Although data anonymization can provide some privacy protection, end-to-end encryption may be more thorough. The privacy of individuals may be jeopardized if anonymized data is still vulnerable to re-identification attacks or correlation with other datasets. A further security risk is introduced by maintaining the mapping between anonymized and original data, as unauthorized access to this mapping could lead to the re-identification of people.
Option C is correct. It includes the top recommendations for safeguarding PII in a pipeline used to produce sensitive financial data. Encryption from beginning to end is used to guarantee data security during both transit and storage. Algorithms for encryption and secure key management systems further increase the pipeline’s security. Strict access controls, monitoring tools, and routine security audits assist in tracking and auditing data access and ensuring compliance with privacy laws.
Option D is incorrect. It suggests using data redaction techniques to remove or mask sensitive PII. Even though data redaction has its uses, it might not offer the same level of security as end-to-end encryption. Redaction involves removing or masking PII, but there is always a chance that there will be leftover data in the dataset that could be used or reconstructed. The pipeline becomes more complex as a result of the careful monitoring and review that redaction requires to ensure accuracy and compliance.
Reference:
Domain: Security and Governance
Question 24. You are a senior data engineer handling the role of database administrator for a healthcare organization that manages sensitive patient data. To manage its data assets and ensure quick data access for analytics, the organization has implemented Databricks Unity Catalog. You must assign specific permissions to various user roles within the organization as part of your duties while abiding by stringent data security and privacy laws. Data Manager, Data Scientist, and Data Analyst are the three user roles available within the organization. The patient demographics table, which contains columns for the patient’s name, age, and gender, should only have read-only access for the Data Analyst role. Access to the patient health records table, which contains private data like medical diagnoses and treatment specifics, is necessary for the Data Scientist role. For administrative purposes, the Data Manager role requires full access to every table in the Unity Catalog. Which of the following options, in the given scenario, offers the most secure and appropriate permission grants for each user role?
A. Grant SELECT permissions on the patient demographics table to the Data Analyst role. Grant INSERT, UPDATE, and DELETE permissions on the patient health records table to the Data Scientist role. Grant full access (SELECT, INSERT, UPDATE, DELETE) to all tables in the Unity Catalog to the Data Manager role.
B. Grant SELECT, INSERT, UPDATE, and DELETE permissions on the patient demographics table to the Data Analyst role. Grant SELECT, INSERT, UPDATE, and DELETE permissions on the patient health records table to the Data Scientist role. Grant full access (SELECT, INSERT, UPDATE, DELETE) to all tables in the Unity Catalog to the Data Manager role.
C. Grant SELECT, INSERT, UPDATE, and DELETE permissions on the patient demographics table to the Data Analyst role. Grant SELECT, INSERT, UPDATE, and DELETE permissions on the patient health records table to the Data Scientist role. Grant SELECT, INSERT, UPDATE, and DELETE permissions on all tables in the Unity Catalog to the Data Manager role.
D. Grant SELECT, INSERT, and UPDATE permissions on the patient demographics table to the Data Analyst role. Grant SELECT, INSERT, and UPDATE permissions on the patient health records table to the Data Scientist role. Grant full access (SELECT, INSERT, UPDATE, DELETE) to all tables in the Unity Catalog to the Data Manager role.
Correct Answer: D
Explanation:
Option A is incorrect. According to the specifications, this choice gives the Data Analyst role SELECT permissions on the patient demographics table. However, it also gives the Data Scientist role additional access privileges, such as the ability to INSERT, UPDATE, and DELETE records from the patient health records table. Additionally, since the Data Manager role only needs administrative access and not full control over all tables, giving it full access (SELECT, INSERT, UPDATE, DELETE) to all tables in the Unity Catalog could pose unnecessary security risks.
Option B is incorrect. The Data Analyst and Data Scientist roles receive an excessive amount of permissions under this option. It goes beyond the requirements to grant SELECT, INSERT, UPDATE, and DELETE permissions on the patient demographics and patient health records tables. Although the permissions granted to the Data Manager role are correct, this option does not follow the rule of least privilege for the other roles.
Option C is incorrect. The Data Analyst and Data Scientist roles, respectively, will receive the necessary permissions on the patient demographics and patient health records tables when choosing this option. In contrast, it also gives the Data Manager role access to all tables in the Unity Catalog, which is more access than is necessary for administrative duties. According to the least privilege principle, the Data Manager role should only have the rights required for administrative tasks.
Option D is correct. It is the safest and most suitable option in this situation. The Data Analyst role is given SELECT, INSERT, and UPDATE permissions on the patient demographics table, which is in line with their need for read-only access. Additionally, it gives the Data Scientist role the SELECT, INSERT, and UPDATE permissions on the patient health records table, satisfying their requirement for read and write access to the sensitive data. Finally, granting the Data Manager role full access (SELECT, INSERT, UPDATE, DELETE) to all tables in the Unity Catalog enables them to efficiently carry out their administrative duties. By granting each role only those privileges necessary to carry out their duties, this option adheres to the principle of least privilege.
Reference:
https://docs.databricks.com/data-governance/unity-catalog/index.html#admin-roles-for-unity-catalog
https://docs.databricks.com/data-governance/unity-catalog/manage-privileges/index.html
Domain: Monitoring and Logging
Question 25. A Spark application running on a cluster is experiencing performance issues and is not meeting its SLA. A data engineer suspects that the issue is related to data skew. Which Spark UI feature can help the data engineer diagnose this problem?
A. The “Storage” tab in the Spark UI
B. The “Event Timeline” tab in the Spark UI
C. The “Environment” tab in the Spark UI
D. The “SQL” tab in the Spark UI
Correct Answer: A
Explanation:
Option A is correct. RDDs (Resilient Distributed Datasets) and DataFrames used in the Spark application are described in detail concerning their storage level on the “Storage” tab in the Spark UI. It displays the size, location of the data’s storage, and its partitioning. A data skew issue, such as uneven data distribution across partitions or an unbalanced workload on some partitions, can be found by analyzing this data. The data engineer can use this to identify and fix performance problems caused by data skew.
Option B is incorrect. The “Event Timeline” tab in the Spark UI shows a timeline of different events, such as tasks, stages, and shuffle operations, that take place while the Spark application is being executed. While understanding the overall execution flow and locating bottlenecks can be aided by this information, it might not give you any particular insights into problems with data skew.
Option C is incorrect. The configuration, system properties, and environment variables of the application’s environment are detailed in the “Environment” tab of the Spark UI. While this knowledge is useful for comprehending the Spark application’s overall setup, it is not specifically helpful for identifying data skew problems.
Option D is incorrect. Applications that use Spark SQL to query and process data should use the “SQL” tab in the Spark UI. It shows query metrics, the execution plan for SQL queries, and other SQL-related data. While this tab can offer information about query performance and optimization, it might not be the best tool for identifying data skew problems.
Reference:
https://spark.apache.org/docs/latest/web-ui.html
Domain: Model Lifecycle Management
Question 26. A machine learning engineer has trained and logged a random forest classifier model using scikit-learn. After terminating the training cluster, they want to retrieve the feature_importances_ attribute of the trained model to understand the importance of each feature in the classification task. Which of the following lines of code can be used to restore the model object and access the feature_importances_ attribute?
A. mlflow.sklearn.load_model(model_uri)
B. client.pyfunc.load_model(model_uri)
C. mlflow.load_model(model_uri)
D. client.list_artifacts(run_id)[“feature_importances.pkl”]
E. This information can only be viewed in the MLflow Experiments UI
Correct Answer: A
Explanation:
Option A is correct because, this is the correct way to load a scikit-learn model that was logged with MLflow. The mlflow.sklearn module provides utilities for logging and loading scikit-learn models, and load_model restores the model object. Once the model is loaded, you can access its attributes, like feature_importances_
Option B is incorrect because this line of code is used to load a Python function model, not a scikit-learn model. The pyfunc module in MLflow is for loading and evaluating Python functions, not specific machine learning model objects.
Option C is incorrect because this line of code is ambiguous and can lead to errors. The mlflow.load_model function requires a flavor parameter to specify the model type (e.g., mlflow.sklearn.load_model for scikit-learn models). Using mlflow.load_model without specifying the flavor may cause issues.
Option D is incorrect because this line of code assumes that the feature_importances_ attribute was saved as a separate artifact file named “feature_importances.pkl”. While it’s possible to save model attributes as artifacts, it’s not a recommended practice. The preferred way is to load the entire model object using mlflow.sklearn.load_model.
Option E is incorrect because, while the MLflow Experiments UI can display information about logged runs and models, it does not provide access to model object attributes like feature_importances_. To access these attributes programmatically, you need to load the model object using the appropriate MLflow utility function (e.g., mlflow.sklearn.load_model).
Reference:
https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html
Domain: Model Lifecycle Management
Question 27. A data scientist wants to retrieve a list of all the registered models in the MLflow Model Registry.
They have the following code snippet:
from mlflow.utils.rest_utils import http_request
endpoint = “/api/2.0/mlflow/registered-models/search”
response = http_request(
host_creds=host_creds,
endpoint=endpoint,
method=”POST”
)
Which of the following modifications should the data scientist make to the code snippet to achieve the desired task?
A. No changes are required.
B. Replace the endpoint URL with “/api/2.0/mlflow/registered-models/list”.
C. Change the HTTP method from “POST” to “PUT” in the http_request call.
D. Change the endpoint URL to “/api/2.0/mlflow/registered-models/get”.
E. Change the HTTP method from “POST” to “GET” in the http_request call.
Answer: E
Explanation:
Option A is incorrect because the provided code snippet is not correct for retrieving a list of all registered models in the MLflow Model Registry.
Option B is incorrect because the MLflow Model Registry API does not have an endpoint named “/registered-models/list”.
Option C is incorrect because the PUT method is used for updating resources, not retrieving data. Changing the method to PUT would not work for this task.
Option D is incorrect because the MLflow Model Registry API does not have an endpoint named “/registered-models/get”.
Option E is CORRECT because the MLflow Model Registry API uses the GET method for retrieving data, including a list of registered models. The correct endpoint for this task is “/api/2.0/mlflow/registered-models/search”. By changing the HTTP method from POST to GET, the provided code snippet will correctly retrieve a list of all registered models in the MLflow Model Registry.
Reference: https://mlflow.org/docs/latest/model-registry.html
Domain: Model Lifecycle Management
Question 28. A machine learning engineer wants to remove a specific model version from the MLflow Model Registry. Which of the following MLflow commands should they use to accomplish this task?
A. client.delete_model_version
B. client.transition_model_stage
C. client.delete_registered_model_instance
D. client.archive_model_version
E. client.modify_registered_model
Answer: A
Option A is CORRECT because this is the correct command to delete a specific model version from the MLflow Model Registry. The delete_model_version function takes the name of the registered model and the version number as arguments and removes that model version from the registry.
Option B is incorrect because this command is used to transition a model version to a different stage (e.g., from “Staging” to “Production”), not to delete a model version entirely.
Option C is incorrect because there is no such command in the MLflow client API. The correct command to delete an entire registered model (including all its versions) is delete_registered_model
Option D is incorrect because the MLflow client API does not have a function named archive_model_version. The correct command to delete a specific model version is remove_model_version.
Option E is incorrect because this command is used to modify the description or rename a registered model, but it cannot be used to delete a model version.
Reference: https://mlflow.org/docs/latest/model-registry.html
Domain: Model Lifecycle Management
Question 29: When using an MLflow Pyfunc model for prediction, what is the purpose of the context parameter in the predict method?
A. The context parameter enables logging of model performance metrics during inference.
B. The context parameter allows customizing the model’s decision logic with user-defined code.
C. The context parameter provides a way to include business context information for downstream model consumers.
D. The context parameter grants the model access to auxiliary objects like preprocessors or configurations.
E. The context parameter determines which version of the registered MLflow model should be used for prediction.
Answer: D
Explanation:
Option A is incorrect because the context parameter is not used for logging model performance metrics. Logging metrics is typically done separately using MLflow’s tracking functionality.
Option B is incorrect because the context parameter does not provide a way to modify the model’s decision logic or implement custom if-else logic. The model’s logic is defined during training and remains fixed during inference.
Option C is incorrect because while the context parameter can be used to pass additional information, it is not specifically intended for including business context information for downstream consumers.
Option D is CORRECT because this is the correct purpose of the context parameter in the predict method of MLflow Python models. It allows passing in auxiliary objects, such as data preprocessors or model configurations, that the model might need during inference.
Option E is incorrect because the context parameter does not determine which version of the registered MLflow model to use. The version is typically specified when loading the model using the MLflow client API.
Reference: https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
Domain: Model Lifecycle Management
Question 30: A data scientist has developed a custom preprocessing class FeatureTransformer that performs feature engineering on input data. They have integrated this class into their machine learning pipeline by wrapping it along with their model in a custom class ModelWithTransformer. The data scientist then logs the fitted ModelWithTransformer instance as an MLflow pyfunc model.
When loading the logged pyfunc model for deployment, which of the following statements accurately describes the benefit of this approach?
A. The pyfunc model can leverage distributed computing for scalable predictions.
B. There is no specific advantage to this approach when loading the pyfunc model.
C. The need for separate data preprocessing steps is eliminated.
D. The FeatureTransformer logic will be automatically applied during model training.
E. The FeatureTransformer logic will be automatically applied during model inference.
Answer: E
Explanation:
Option A is incorrect because while MLflow does support distributed computing for certain model types, this is not a direct benefit of wrapping the preprocessing logic within a custom class and logging it as a pyfunc model.
Option B is incorrect because wrapping the preprocessing logic along with the model and logging it as a pyfunc model provides a significant advantage during deployment, as explained in the correct option.
Option C is incorrect because while the preprocessing logic is encapsulated within the custom class, separate preprocessing steps are still required. The custom class provides a way to integrate the preprocessing logic with the model, but it does not eliminate the need for preprocessing altogether.
Option D is incorrect because the question is specifically about the benefits when loading the logged pyfunc model for deployment, not during training.
Option E is CORRECT because this is the primary benefit of wrapping the preprocessing logic within a custom class and logging it as a pyfunc model. When the pyfunc model is loaded for deployment, the same preprocessing logic encapsulated in the FeatureTransformer class will be automatically applied before making predictions with the model.
Reference:
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
Conclusion
We hope that the collection of Databricks Certified Data Engineer Professional Certification exam questions provided in this resource has been valuable in your preparation journey. Remember that success in the exam requires not only knowledge but also practice.
Taking the Databricks Certified Data Engineer Professional Certification exam can be a stepping stone to a successful and fulfilling career in data analytics. You can get practical experience by utilizing our hands-on labs and sandboxes.
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