{"id":81406,"date":"2022-03-04T02:28:53","date_gmt":"2022-03-04T07:58:53","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=81406"},"modified":"2024-04-24T12:58:12","modified_gmt":"2024-04-24T07:28:12","slug":"microsoft-azure-dp-203-exam-questions","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/","title":{"rendered":"Free Questions on DP-203: Data Engineering on Microsoft Azure"},"content":{"rendered":"<p>Came here and looking for <a href=\"https:\/\/www.whizlabs.com\/microsoft-azure-certification-dp-203\/\" target=\"_blank\" rel=\"noopener\">DP-203 exam<\/a> questions? You have certainly landed on the right page. Whizlabs free practice questions not only give you an evaluation of the exam but going through these help you revise the exam-ready concepts.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 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href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#For_Who_this_exam_is_intended_for\" >For Who this exam is intended for?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-2\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_security\" >Domain : Design and implement data security<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-3\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-4\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_security-2\" >Domain : Design and implement data security<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-5\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-2\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Monitor_and_optimize_data_storage_and_data_processing\" >Domain : Monitor and optimize data storage and data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-6\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-3\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-4\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-7\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_security-3\" >Domain : Design and implement data security<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Monitor_and_optimize_data_storage_and_data_processing-2\" >Domain : Monitor and optimize data storage and data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-8\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_security-4\" >Domain : Design and implement data security<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-9\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-5\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-10\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-6\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_develop_data_processing-7\" >Domain : Design and develop data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Design_and_implement_data_storage-11\" >Domain : Design and implement data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Monitor_and_optimize_data_storage_and_data_processing-3\" >Domain : Monitor and optimize data storage and data processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Develop_Data_Processing\" >Domain: Develop Data Processing\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Develop_Data_Processing-2\" >Domain: Develop Data Processing\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Secure_Monitor_and_Optimize_Data_Storage_and_Data_Processing\" >Domain: Secure, Monitor, and Optimize Data Storage and Data Processing\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domian_Design_and_Implement_Data_storage\" >Domian: Design and Implement Data storage\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Domain_Secure_Monitor_and_Optimize_Data_Storage_and_Data_Processing-2\" >Domain: Secure, Monitor, and Optimize Data Storage and Data Processing\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-dp-203-exam-questions\/#Summary\" >Summary<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"What_do_Azure_Data_Engineers_do\"><\/span>What do Azure Data Engineers do?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Azure Data Engineers enable stakeholders in the understanding of Data via exploration. Using different tools and techniques, they enable the development and maintenance of compliant and secure Data Processing Pipelines. They further help with the storage and production of cleansed and enhanced datasets for analysis, using multiple Azure Data Services and Languages.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"For_Who_this_exam_is_intended_for\"><\/span>For Who this exam is intended for?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This exam has been tailor-built for candidates who possess a strong knowledge of Data Processing Languages, like Python, Scala, and SQL. They must have a clear understanding of Data Architecture Patterns and Parallel Processing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The candidates appearing for the DP-203 exam are assumed to be having subject level expertise on the integration, consolidation and transformation of data coming through multiple unstructured and structured data systems, to be formed into a structure capable of building analytics solutions.<\/span><\/p>\n<p><span style=\"font-family: 'Open Sans', arial, sans-serif; font-size: 22px;\">What does this exam comprise of?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The DP-203 exam evaluates a candidate on his ability to implement certain technical tasks including,<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Designing and implementation of Data Storage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring and optimization of Data Storage and Data Processing.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Designing and development of Data Processing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Designing and implementation of Data Security<\/span><\/li>\n<\/ul>\n<p>Ok. Let&#8217;s start learning these DP-203 exam questions now!<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q1 : You have been assigned the task of partitioning the FactOnlineSales table on the OrderDateKey column in the dedicated SQL pool. For this purpose, you decide to use the CREATE TABLE statement.<\/em><\/h4>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-81416 size-full\" title=\"Create Table statement in SQL - Microsoft Azure\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1.png\" alt=\"Create Table statement in SQL - Microsoft Azure\" width=\"617\" height=\"468\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1.png 617w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1-300x228.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1-554x420.png 554w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1-80x60.png 80w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-1-100x75.png 100w\" sizes=\"(max-width: 617px) 100vw, 617px\" \/><\/p>\n<h4><em>Complete the statement by filling the blanks with the right words.<\/em><\/h4>\n<p><strong>A. <\/strong>Distribution and Partition<br \/>\n<strong>B. <\/strong>DistributionTable and PartitionTable<br \/>\n<strong>C. <\/strong>Distribution and Collate<br \/>\n<strong>D. <\/strong>Partition and Distribution<\/p>\n<p><strong>Correct Answer: A<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>DISTRIBUTION = HASH ( <em>distribution_column_name<\/em> )\u00a0 is the distribution method that assigns every row to one distribution by hashing\u00a0 the value present in <em>distribution_column_name<\/em>. The right syntax to use partition method is PARTITION ( <em>partition_column_name<\/em> RANGE [ LEFT | RIGHT ] FOR VALUES ([<em>boundary_value<\/em> [,&#8230;<em>n<\/em>]])).<\/p>\n<p><strong>Option A is correct.<\/strong> Distribution and Partition are the right options to be used to complete the given Create Table statement.<br \/>\n<strong>Option B is incorrect.<\/strong> The right syntax is to use only Distribution and Partition, not DistributionTable and PartitionTable.<br \/>\n<strong>Option C is incorrect<\/strong>. The partition should be used instead of Collate.<br \/>\n<strong>Option D is incorrect.<\/strong> Distribution and Partition are the right options to use.<\/p>\n<p><strong>References: <\/strong>To know more about partitioning the tables, please visit the below-given link:<br \/>\n<a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql-data-warehouse\/sql-data-warehouse-tables-partition\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql-data-warehouse\/sql-data-warehouse-tables-partition<\/a><br \/>\n<a href=\"https:\/\/docs.microsoft.com\/en-us\/sql\/t-sql\/statements\/create-table-azure-sql-data-warehouse?view=aps-pdw-2016-au7\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/t-sql\/statements\/create-table-azure-sql-data-warehouse?view=aps-pdw-2016-au7<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-2\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q2 : You are working on Azure Data Lake Store Gen1. Suddenly, you realize the need to know the schema of the external data. Which of the following plug-in would you use to know the external data schema?<\/em><\/h4>\n<p><strong>A. <\/strong>Ipv4_lookup<br \/>\n<strong>B. <\/strong>Mysql_request<br \/>\n<strong>C. <\/strong>Pivot<br \/>\n<strong>D. <\/strong>Narrow<br \/>\n<strong>E. <\/strong>infer_storage_schema<\/p>\n<p><strong>Correct Answer: E<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/data-explorer\/kusto\/query\/inferstorageschemaplugin\" target=\"_blank\" rel=\"nofollow noopener\">infer_storage_schema<\/a> is the plug-in that helps infer the schema based on the external file contents; when the external data schema is unknown.<\/p>\n<p><strong>Option A is incorrect.<\/strong> The ipv4_lookup plugin checks for an IPv4 value in a lookup table and returns the matched rows.<br \/>\n<strong>Option B is incorrect<\/strong>. The mysql_request plugin transfers a SQL query to a MySQL Server network endpoint and returns the 1st row set in the result.<br \/>\n<strong>Option C is incorrect.<\/strong> Pivot plug-in is used to rotate a table by changing the unique values from 1 column in the input table into a number of different columns in the output table and perform aggregations wherever needed on any remaining column values that are desired in the final output.<br \/>\n<strong>Option D is incorrect<\/strong>. This plug-in is used to unpivot a wide table into a table with only three columns.<br \/>\n<strong>Option E is correct<\/strong>. infer_storage_schema plug-in can be used to infer the schema of external data and return it as a CSL schema string.<\/p>\n<p><strong>References: <\/strong>To know more about the external tables and plug-in, please visit the below-given link:<br \/>\n<a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/data-explorer\/kusto\/management\/external-tables-azurestorage-azuredatalake\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/data-explorer\/kusto\/management\/external-tables-azurestorage-azuredatalake<\/a><br \/>\n<a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/data-explorer\/kusto\/query\/inferstorageschemaplugin\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/data-explorer\/kusto\/query\/inferstorageschemaplugin<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_security\"><\/span>Domain : Design and implement data security<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q3 : <strong>\u00a0<\/strong>You work in Azure Synapse Analytics dedicated SQL pool that has a table titled Pilots. Now you want to restrict the user access in such a way that users in \u2018IndianAnalyst\u2019 role can see only the pilots from India.\u00a0 Which of the following would you add to the solution?<\/em><\/h4>\n<p><strong>A. <\/strong>Table partitions<br \/>\n<strong>B. <\/strong>Encryption<br \/>\n<strong>C. <\/strong>Column-Level security<br \/>\n<strong>D. <\/strong>Row-level security<br \/>\n<strong>E. <\/strong>Data Masking<\/p>\n<p><strong>Correct Answer: D<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Row-level security is applicable on databases to allow fine-grained access to the rows in a database table for restricted control upon who could access which type of data.<\/p>\n<p><strong>Option A is incorrect.<\/strong> Table partitions are generally used to group similar data.<br \/>\n<strong>Option B is incorrect<\/strong>. Encryption is used for security purposes.<br \/>\n<strong>Option C is incorrect.<\/strong> Column level security is used to restrict data access at the column level. In the given scenario, we need to restrict access at the row level.<br \/>\n<strong>Option D is correct.<\/strong> In this scenario, we need to restrict access on a row basis, i.e only for the pilots from India, there Row-level security is the right solution.<br \/>\n<strong>Option E is incorrect<\/strong>. Sensitive data exposure can be limited by masking it to unauthorized users using SQL Database dynamic data masking.<\/p>\n<p><strong>References: <\/strong>To know more about Row-level security, please visit the below-given links:<br \/>\n<a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/<\/a><br \/>\n<a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/techcommunity.microsoft.com\/t5\/azure-synapse-analytics\/how-to-implement-row-level-security-in-serverless-sql-pools\/ba-p\/2354759<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-3\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q4 : While working on the project, you realize that the delta table is not correct. One of your friends suggests deleting the whole directory of the table and creating a new table on the same path. Would you follow the suggested solution?<\/em><\/h4>\n<p><strong>A. <\/strong>Yes<br \/>\n<strong>B. <\/strong>No<\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Deleting the whole directory of a Delta table and creating a new table on the same path is not a recommended solution as:<\/p>\n<p>A directory may consist of very large files and deleting the directory can consume days or even hours. Therefore, it is not an efficient solution.<br \/>\nAll the content of the deleted files is lost and if by mistake you delete a wrong file, it is very hard to recover it.<br \/>\nDeleting the directory is not atomic. While table deletion is in progress, a concurrent query reading the table can view a partial table or even fail.<\/p>\n<p><strong>Reference: <\/strong>To know more about best practices while using Delta Lake, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/databricks\/delta\/best-practices<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-4\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q5 : The partition specifies how Azure storage load balances entities, messages, and blobs across servers to achieve the traffic requirements of these objects. Which of the following represents the partition key for a blob?<\/em><\/h4>\n<p><strong>A. <\/strong>Account name + Table Name + blob name<br \/>\n<strong>B. <\/strong>Account name + container name + blob name<br \/>\n<strong>C. <\/strong>Account name + Queue name + blob name<br \/>\n<strong>D. <\/strong>Account Name + Table Name + Partition Key<br \/>\n<strong>E. <\/strong>Account Name + Queue Name<\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>For a blob, the partition key consists of account name + container name + blob name. Data is partitioned into ranges using these partition keys and these ranges are load balanced throughout the system.<\/p>\n<p><strong>Option A is incorrect.<\/strong> For a blob, the partition key includes account name + container name + blob name.<br \/>\n<strong>Option B is correct<\/strong>. For a blob, the partition key includes account name + container name + blob name.<br \/>\n<strong>Option C is incorrect<\/strong>. Account name + Queue name + blob name is not the right partition key for a blob.<br \/>\n<strong>Option D is incorrect.<\/strong> For an entity in a table, the partition key includes the table name and the partition key.<br \/>\n<strong>Option E is incorrect.<\/strong> For a message in a queue, the queue name is the partition key itself.<\/p>\n<p><strong>Reference: <\/strong>To know more about Partitioning Azure Blob Storage, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/architecture\/best-practices\/data-partitioning-strategies<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q6 : Data Cleansing in Data Quality Services (DQS) includes a 2-step process for data cleansing: computer-assisted and interactive cleansing. Depending upon the computer-assisted cleansing process, during the interactive cleansing, DQS gives the data steward with information that is needed to make a decision about modifying or changing the data. For this purpose, DQS classifies the data into 5 tabs. From the below-given option, choose the tab that is not among these tabs.<\/em><\/h4>\n<p><strong>A. <\/strong>Invalid<br \/>\n<strong>B. <\/strong>Valid<br \/>\n<strong>C. <\/strong>Suggested<br \/>\n<strong>D. <\/strong>New<br \/>\n<strong>E. <\/strong>Correct<br \/>\n<strong>F. <\/strong>Corrected<\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>During the interactive cleansing, Data Quality Services (DQS) classifies the data in these 5 tabs: Suggested, New, Invalid, Corrected, and Correct.<\/p>\n<p><strong>Option A is incorrect.<\/strong> Invalid tab has the values that were specified as invalid in the domain in the knowledge base or values that failed reference data or a domain rule.<br \/>\n<strong>Option B is correct<\/strong>. There is no such tab with the name valid.<br \/>\n<strong>Option C is incorrect.<\/strong> The suggested tab consists of the values having a confidence level greater than the <em>auto-suggestion threshold<\/em> value but lesser than the <em>auto-correction threshold<\/em> value for which Data Quality Services (DQS) found suggestions.<br \/>\n<strong>Option D is incorrect.<\/strong> The new tab consists of the valid values for which Data Quality Services (DQS) doesn\u2019t have sufficient information (suggestion), and hence can\u2019t be mapped to any other tab.<br \/>\n<strong>Option E is incorrect.<\/strong> The correct tab is for the values which were found correct.<br \/>\n<strong>Option F is incorrect.<\/strong> The corrected tab is for the values that are corrected by Data Quality Services (DQS) during the automated cleansing.<\/p>\n<p><strong>Reference: <\/strong>To know more about data cleansing, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/data-quality-services\/data-cleansing?view=sql-server-ver15<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_security-2\"><\/span>Domain : Design and implement data security<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q7 : You need to design an enterprise data warehouse in Azure SQL Database with a table titled customers. You need to ensure that the customer supportive staff can identify the customers by matching the few characters of their email addresses but the full email addresses of the customers should not be visible to them. Which of the following would you include in the solution?<\/em><\/h4>\n<p><strong>A. <\/strong>Row-level security<br \/>\n<strong>B. <\/strong>Encryption<br \/>\n<strong>C. <\/strong>Column Level Security<br \/>\n<strong>D. <\/strong>Dynamic Data Masking<br \/>\n<strong>E. <\/strong>Any of the above can be used<\/p>\n<p><strong>Correct Answer: D<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Dynamic data masking is helpful in preventing unauthorized access to sensitive data by empowering the clients to specify how much of the sensitive data to disclose with minimum impact on the application layer. In this policy-based security feature, the sensitive data is hidden in the output of a query over specified database fields, but there is no change in the data in the database.<\/p>\n<p>For example: *******abc@gmail.com<\/p>\n<p><strong>Option A is incorrect<\/strong>. Row-level security is used to enable the restricted access i.e who can access what type of data.<br \/>\n<strong>Option B is incorrect. <\/strong>Encryption is not the right solution.<br \/>\n<strong>Option C is incorrect.<\/strong> Column level security won&#8217;t help in limiting the exposure of sensitive data.<br \/>\n<strong>Option D is correct<\/strong>. In the given scenario, there is a need to use Dynamic data masking to limit the sensitive data exposure to non-privileged users.<br \/>\n<strong>Option E is incorrect.<\/strong> Dynamic Data Masking is the right answer.<\/p>\n<p><strong>Reference: <\/strong>To know more about dynamic data masking, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/azure-sql\/database\/dynamic-data-masking-overview<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-5\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q8 : You need to create a quick object in a test environment and therefore you decide to create a temporal table with an &#8220;anonymous&#8221; history table. From the given below statement\/remarks about the history table in this context, choose the statement(s) that is\/are true.<\/em><\/h4>\n<p><strong>A. <\/strong>You need to manually create the anonymous history table and provide its specific schema<br \/>\n<strong>B. <\/strong>The history table is created as a rowstore table<br \/>\n<strong>C. <\/strong>The history table is created as a columnstore table<br \/>\n<strong>D. <\/strong>A default clustered index is developed for the history table<br \/>\n<strong>E. <\/strong>A history table is always uncompressed. No compression is ever applied on the history table<\/p>\n<p><strong>Correct Answers: B and D<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>An anonymous history table is automatically built in the same schema as the temporal or current table. The history table is built as a rowstore table. If possible, page compression is applied on the history table otherwise the table remains uncompressed. For example, few table configurations, like SPARSE columns, don\u2019t allow compression.<\/p>\n<p><strong>Option A is incorrect<\/strong>. An anonymous history table is automatically built in the same schema as the temporal or current table.<br \/>\n<strong>Option B is correct.<\/strong> It is true that the history table is built as a rowstore table.<br \/>\n<strong>Option C is incorrect.<\/strong> The history table is created as a rowstore table, not columnstore table.<br \/>\n<strong>Option D is correct.<\/strong> A default clustered index is developed for the history table with an auto-generated name with the format <em>IX_&lt;history_table_name&gt;<\/em>. This index has the PERIOD columns (end, start).<br \/>\n<strong>Option E is incorrect<\/strong>. It is not true that the history table always remains uncompressed. If possible, page compression is applied on the history table otherwise the table remains uncompressed. For example, few table configurations, like SPARSE columns, don&#8217;t allow compression.<\/p>\n<p><strong>Reference: <\/strong>To know more about creating a system-versioned temporal table, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/relational-databases\/tables\/creating-a-system-versioned-temporal-table?view=sql-server-ver15<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-2\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q9 : There are a number of various analytical data stores that use different languages, models, and provide different capabilities. Which of the following is a low-latency NoSQL data store that provides a high-performance and flexible option to query structured and semi-structured data?<\/em><\/h4>\n<p><strong>A. <\/strong>Azure Synapse Analytics<br \/>\n<strong>B. <\/strong>HBase<br \/>\n<strong>C. <\/strong>Spark SQL<br \/>\n<strong>D. <\/strong>Hive<br \/>\n<strong>E. <\/strong>None of these<\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>HBase is a low-latency NoSQL data store that provides a high-performance and flexible option to query structured and semi-structured data. The primary data model used by HBase is the Wide column store.<\/p>\n<p><strong>Option A is incorrect.<\/strong> Azure Synapse is a managed service depending upon the SQL Server database technologies and is optimized for supporting large-scale data warehousing workloads.<br \/>\n<strong>Option B is correct<\/strong>. HBase is a low-latency NoSQL data store that provides a high-performance and flexible option to query structured and semi-structured data.<br \/>\n<strong>Option C is incorrect.<\/strong> Spark SQL is an API developed on Spark that enables the creation of data frames and tables which are possible to be queried using SQL syntax.<br \/>\n<strong>Option D is incorrect.<\/strong> It is HBase, not Hive that is a low-latency NoSQL data store that provides a high-performance and flexible option to query structured and semi-structured data.<br \/>\n<strong>Option E is incorrect.<\/strong> HBase is the right answer.<\/p>\n<p><strong>Reference: <\/strong>To know more about batch processing, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/architecture\/data-guide\/big-data\/batch-processing<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Monitor_and_optimize_data_storage_and_data_processing\"><\/span>Domain : Monitor and optimize data storage and data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q10 : You work in an Azure Transformational Logics (ATL) company and you have been given the responsibility to create and update query-optimization statistics utilizing the Synapse SQL resources in a dedicated SQL pool.\u00a0 The following are the guiding principles recommended for updating the statistics during the load process. Which of the following is\/are not true?<\/em><\/h4>\n<p><strong>A. <\/strong>Ensure that every loaded table is having at least 1 statistics object updated<br \/>\n<strong>B. <\/strong>Focus on the columns participating in ORDER BY, GROUP BY, JOIN and DISTINCT clauses<br \/>\n<strong>C. <\/strong>Update &#8220;ascending key&#8221; columns like order dates more frequently as these values are not considered\/included in the statistics histogram<br \/>\n<strong>D. <\/strong>Update static distribution columns more frequently<br \/>\n<strong>E. <\/strong>None of these<\/p>\n<p><strong>Correct Answer: D<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>The below-given guiding principles are recommended to update the statistics during the load process:<\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-81417\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10.png\" alt=\"\" width=\"928\" height=\"214\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10.png 928w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10-300x69.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10-768x177.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10-640x148.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-10-681x157.png 681w\" sizes=\"(max-width: 928px) 100vw, 928px\" \/><\/p>\n<p><strong>Option A is incorrect<\/strong>. It is true that you should ensure that every loaded table is having at least 1 statistics object updated.<br \/>\n<strong>Option B is incorrect<\/strong>. The given one is also a guiding principle to update the statistics during the load process.<br \/>\n<strong>Option C is incorrect.<\/strong> The given one is also a guiding principle to update the statistics during the load process.<br \/>\n<strong>Option D is correct<\/strong>. Rather than more frequently, you need to update static distribution columns less frequently.<br \/>\n<strong>Option E is incorrect.<\/strong> Option D describes the wrong principle.<\/p>\n<p><strong>Reference: <\/strong>To know more about Statistics in Synapse SQL, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql\/develop-tables-statistics#update-statistics<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-6\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q11 : There are a number of different options for data serving storage in Azure. These options vary based on the capability they offer. Which of the below-given options don&#8217;t offer Row-Level security?<\/em><\/h4>\n<p><strong>A. <\/strong>SQL Database<br \/>\n<strong>B. <\/strong>Azure Data Explorer<br \/>\n<strong>C. <\/strong>HBase\/Phoenix on HDInsight<br \/>\n<strong>D. <\/strong>Hive LLAP on HDInsight<br \/>\n<strong>E. <\/strong>Azure Analysis Services<br \/>\n<strong>F. <\/strong>Cosmos DB<\/p>\n<p><strong>Correct Answers: B and F<\/strong><\/p>\n<p><strong>Explanation: <\/strong><\/p>\n<p>The below table mentions the various security capabilities offered by different data serving storage options.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-81418 size-full\" title=\"Security capabilities in Microsoft Azure\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11.png\" alt=\"Security capabilities in Microsoft Azure\" width=\"966\" height=\"561\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11.png 966w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11-300x174.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11-768x446.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11-723x420.png 723w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11-640x372.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-11-681x395.png 681w\" sizes=\"(max-width: 966px) 100vw, 966px\" \/><\/p>\n<p><strong>Option A is incorrect.<\/strong> SQL Database offers Row-level security.<br \/>\n<strong>Option B is correct.<\/strong> Azure Data Explorer doesn\u2019t provide Row-level security.<br \/>\n<strong>Option C is incorrect.<\/strong> HBase\/Phoenix on HDInsight offers Row-level security with domain-joined HDInsight clusters.<br \/>\n<strong>Option D is incorrect.<\/strong> Hive LLAP on HDInsight offers Row level security with domain-joined HDInsight clusters.<br \/>\n<strong>Option E is incorrect<\/strong>. Azure Analysis Services offers Row-level security.<br \/>\n<strong>Option F is correct.<\/strong> Cosmos DB doesn\u2019t provide Row-level security.<\/p>\n<p><strong>Reference: <\/strong>To know more about analytical data stores in Azure, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/architecture\/data-guide\/technology-choices\/analytical-data-stores<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-3\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q12 : When you implement the Clean Missing Data module to a set of data, the Minimum missing value ratio and Maximum missing value ratio are two important factors in replacing the missing values.\u00a0 If the Maximum missing value is set to 1, what does it mean?<\/em><\/h4>\n<p><strong>A. <\/strong>missing values are cleaned only when 100% of the values in the column are missing<br \/>\n<strong>B. <\/strong>missing values are cleaned even if there is only one missing value<br \/>\n<strong>C. <\/strong>missing values are cleaned only when there is only one missing value<br \/>\n<strong>D. <\/strong>missing values won\u2019t be cleaned<br \/>\n<strong>E. <\/strong>missing values are cleaned even if 100% of the values in the column are missing<\/p>\n<p><strong>Correct Answer: E<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Maximum missing value ratio is specified as the maximum number of missing values that can be present for the operation that is to be executed. By default, the Maximum missing value ratio is set to 1 which indicates that missing values will be cleaned even if 100% of the values in the column are missing.<\/p>\n<p><strong>Option A is incorrect.<\/strong> The use of the word \u201cOnly When\u201d does not rightly state the meaning.<br \/>\n<strong>Option B is incorrect.<\/strong> Setting Minimum missing value ratio property to 0 actually means that missing values are cleaned even if there is only one missing value.<br \/>\n<strong>Option C is incorrect.<\/strong> Minimum and Maximum missing value ratios talk only about minimum and maximum ratios, not a specific number.<br \/>\n<strong>Option D is incorrect.<\/strong> The given statement is not right.<br \/>\n<strong>Option E is correct<\/strong>. Setting the Maximum missing value ratio to 1 indicates that missing values will be cleaned even if 100% of the values in the column are missing.<\/p>\n<p><strong>Reference: <\/strong>To know more about the clean missing Data Module, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/algorithm-module-reference\/clean-missing-data<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-4\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q13 : On each file upload, Batch writes 2 log files to the compute node. These log files can be examined to know more about a specific failure. These two files are:<\/em><\/h4>\n<p><strong>A. <\/strong>fileuploadin.txt and fileuploaderr.txt<br \/>\n<strong>B.<\/strong>\u00a0fileuploadout.txt and fileuploadin.txt<br \/>\n<strong>C.<\/strong>\u00a0fileuploadout.txt and fileuploaderr.txt<br \/>\n<strong>D.<\/strong>\u00a0fileuploadout.JSON and fileuploaderr.JSON<br \/>\n<strong>E.<\/strong>\u00a0fileupload.txt and fileuploadout.txt<\/p>\n<p><strong>Correct Answer: C<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>When you upload a file, 2 log files are written by Batch to the compute node, named &#8211; fileuploadout.txt and fileuploaderr.txt. These log files help to get information about a specific failure. The scenarios where file upload is not done, these fileuploadout.txt and fileuploaderr.txt log files don\u2019t exist.<\/p>\n<p><strong>Option A is incorrect<\/strong>. fileuploadin.txt and fileuploaderr.txt are not the right files.<br \/>\n<strong>Option B is incorrect.<\/strong>txt and fileuploadin.txt are not the right log files.<br \/>\n<strong>Option C is correct<\/strong>. On each file upload, Batch writes 2 log files to the compute node. These files are fileuploadout.txt and fileuploaderr.txt.<br \/>\n<strong>Option D is incorrect.<\/strong>JSON and fileuploaderr.JSON are not the right log files.<br \/>\n<strong>Option E is incorrect<\/strong>. fileupload.txt and fileuploadout.txt are not the right files.<\/p>\n<p><strong>Reference: <\/strong>To know more about job and task error checking, please visit the below given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/batch\/batch-job-task-error-checking<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-7\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q14 : You have been assigned the task to manage the storage of consumers profiles and Sales data.\u00a0 A general request is to create a list of \u201cthe top 100 consumers including name, account number and sales around for a specific time period\u201d or \u201cwho are the consumers within a particular geographic region?\u201d<\/em><br \/>\n<em>Is Azure Blob storage a recommended choice for this data?<\/em><\/h4>\n<p><strong>A. <\/strong>Yes<br \/>\n<strong>B. <\/strong>No<\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Blob is not a recommended choice for structured data that needs to be queried regularly. Blobs have higher latency than memory and local disk and also do not have the indexing feature that increases the databases&#8217; efficiency at running queries.<\/p>\n<p><strong>Reference: <\/strong>To know more about blobs, please visit the below-given link: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/storage\/blobs\/storage-blobs-introduction<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_security-3\"><\/span>Domain : Design and implement data security<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q15 : A famous online payment gateway provider is creating a new product where the users can pay their credit card bills and earn reward coins. As part of compliance, they need to ensure that all the data, including credit card details and PIIs, are securely kept. This product is backed by a dedicated SQL pool in azure Synapse analytics. The major concern is that the database team that performs maintenance should not be able to view the customer\u2019s info. Which of the following can be the best solution?<\/em><\/h4>\n<p><strong>A. <\/strong>Implement Transparent data encryption<br \/>\n<strong>B. <\/strong>Use Azure Defender for SQL<br \/>\n<strong>C. <\/strong>Use Dynamic data masking (DDM)<br \/>\n<strong>D. <\/strong>Assign only SQL security manager role to maintenance team members<\/p>\n<p><strong>Correct Answer: C<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Here there is a lot of critical data and personal information involved. Dynamic data masking is the best solution for this. Consider the case of credit card numbers; using DDM, we can actually hide the numbers in that particular column. For example, if the credit card number is 1234 5678 then the displayed value will be like XXXX XX78. Similarly, we can use masking for other data in other columns where PII is present.\u00a0 The maintenance team with limited permissions will only see the covered data and thus, the data is safe from exploitation.<\/p>\n<p><strong>Option A incorrect: <\/strong>Transparent data encryption is a method used by Azure in its relational database services for encrypting data at rest. This will not be the best solution here.<br \/>\n<strong>Option B is incorrect<\/strong>: Azure defender is mainly used to mitigate potential DB vulnerabilities and detect anomalous activities.<br \/>\n<strong>Option C is correct<\/strong>: DDM can hide the data columns as required.<br \/>\n<strong>Option D is incorrect<\/strong>: Assigning Azure security manager role will grant them access to security features configuration, including the ability to enable or disable DDM. This is exactly the opposite of what is required here.<\/p>\n<p><strong>Reference: <\/strong>To know more about DDM, please refer to the doc below: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/azure-sql\/database\/dynamic-data-masking-overview<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Monitor_and_optimize_data_storage_and_data_processing-2\"><\/span>Domain : Monitor and optimize data storage and data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q16 : You have a Serverless SQL pool development assigned by your company. This should follow the best practices and optimized solutions. Which of the following solutions will help you increase the performance?<\/em><\/h4>\n<p><strong>A. <\/strong>Use the same region for Azure Storage account and serverless SQL pool<br \/>\n<strong>B. <\/strong>Convert CSV to Parquet<br \/>\n<strong>C. <\/strong>Use CETAS<br \/>\n<strong>D. <\/strong>Use azure storage throttling<\/p>\n<p><strong>Correct Answers: A, B and C<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>When Azure Storage account and serverless SQL pool are co-located, the latency of loading will be reduced. Thus, there will be an increase in total performance. In situations where these are in different regions, data has to travel more, increasing the latency.<\/p>\n<p>Parquet is columnar formats and compressed and have a smaller size than CSV. So, the time to read it will be less.<\/p>\n<p>CETAs are parallel operations that create external table metadata and export the result of the SELECT query to a set of files in your storage account. We can enhance the query performances.<\/p>\n<p><strong>Options A, B and C are correct: <\/strong>These are the best practices followed to improve the performance of the Serverless SQL pool.<br \/>\n<strong>Option D is incorrect<\/strong>: Storage throttling detection will slow down the SQL pool, and thus the performance will be decreased.<\/p>\n<p><strong>Reference: <\/strong>To know more about Serverless SQL pool development best practices, please refer to the doc below: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql\/develop-best-practices#serverless-sql-pool-development-best-practices<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-8\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q17 : You have a traditional data warehouse storage with a snowflake schema with row-oriented storage that takes considerable time and low performance during queries. You plan to use clustered columnstore indexing. Will it improve query performance?<\/em><\/h4>\n<p><strong>A. <\/strong>Yes<br \/>\n<strong>B. <\/strong>No<\/p>\n<p><strong>Correct Answer: A<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Most of the traditional data warehouses use row-oriented storage. But columnstore indexes are used in modern data warehouses as the standard for storage and query in big data warehousing fact tables.<\/p>\n<p>There are two advantages of using this while comparing with a traditional row-oriented Data warehouse.<\/p>\n<p>10x performance in query performance<br \/>\n10 x data compression<br \/>\n<strong>Option A is correct: <\/strong>Using clustered columnstore will increase the query performance.<\/p>\n<p><strong>Reference: <\/strong>For more details on columnstore indexes, please refer to the following document: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/relational-databases\/indexes\/columnstore-indexes-overview?view=sql-server-ver15<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_security-4\"><\/span>Domain : Design and implement data security<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q18 : A famous fintech startup is setting up its data solution using Azure Synapse analytics. As part of compliance, the company has decided that only the finance managers should be able to see the Bank Account Number and not anyone else. Which of the following is best suited in this scenario?<\/em><\/h4>\n<p><strong>A. <\/strong>Firewall rules to block IP<br \/>\n<strong>B. <\/strong>Row-level security<br \/>\n<strong>C. <\/strong>Column-level security<br \/>\n<strong>D. <\/strong>Azure RBAC role<\/p>\n<p><strong>Correct Answer: C<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Column-level security will control the access to particular columns based on the user membership. In the case of sensitive data, we can decide which user or group can access a particular column. In this question, the restriction should be given to Bank account numbers. So, ideally column-level security can be used.<\/p>\n<p><strong>Option A is incorrect: <\/strong>Firewall block will completely block access to the database.<br \/>\n<strong>Option B is incorrect<\/strong>: Row-level security will prevent access to row and is not required.<br \/>\n<strong>Option C is correct<\/strong>: It will be the best solution.<br \/>\n<strong>Option D is incorrect<\/strong>: Azure RBAC cannot control access to a particular column.<\/p>\n<p><strong>Reference: <\/strong>To know more, please refer to the docs below: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql-data-warehouse\/column-level-security<\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-9\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q19 : A famous IOT devices company collects the metadata about its sensors in the field in reference data. Which of the following services can be used as input for this type of data?<\/em><\/h4>\n<p><strong>A. <\/strong>Azure SQL<br \/>\n<strong>B. <\/strong>Blob Storage<br \/>\n<strong>C. <\/strong>Azure Event Hub<br \/>\n<strong>D. <\/strong>Azure IOT hub<\/p>\n<p><strong>Correct Answers: A and B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Reference data is a fixed data set that is static or, in some cases, changes slowly. Here the metadata values of sensors are slowly changing and thus can be considered as reference data as the question tells. Azure blob storage can ingest this type of data, in which the data is modeled to be a sequence of blobs in ascending order of the date\/time specified in the blob name. Similarly, Azure SQL also can intake reference data. In this case, the data is retrieved by Job in stream analytics and is stored with snapshot memory for further processing.<\/p>\n<p><strong>Options A and B correct: <\/strong>Azure SQL and Blob storage are supported input services for reference data.<br \/>\n<strong>Options C and D incorrect<\/strong>: Azure Event Hub and IoT hub are not supported.<\/p>\n<p><strong>Reference: <\/strong>To know more, please refer to the docs below: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/stream-analytics\/stream-analytics-use-reference-data<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-5\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q20 : You have an Azure Data Factory with Self Hosted Runtime Integration Runtime (SHIR) set up on Azure VM. During a regional failure\/disaster, what is your best option for Data redundancy?<\/em><\/h4>\n<p><strong>A. <\/strong>Utilize Microsoft managed Regional Failover by Azure Data factory<br \/>\n<strong>B. <\/strong>Use Azure Site Recovery for VM failover<br \/>\n<strong>C. <\/strong>Data is by default replicated to the paired region for Azure VM and does automatic failover<br \/>\n<strong>D. <\/strong>Utilize automatic Regional Failover for Azure VM<\/p>\n<p><strong>Correct Answer<\/strong>: <strong>B<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p>Data redundancy is essential in the case of critical workloads. Azure Data Factory with default Azure managed Integration runtime environment has an option for automatic \/ Microsoft managed failover. So, if a disaster or any other thing is causing a region failure, Microsoft managed failover occurs in the paired region. Then you will be able to access the Azure Data Factory resources after that.<\/p>\n<p>But in this case, the Integration runtime is SHIR. And it is using Azure VMs for its infrastructure.<\/p>\n<p>In this case, the best option will be to configure Azure Site Recovery for Azure VMs and create automatic\/ manual cutover to the failover region.<\/p>\n<p><strong>Option A is incorrect: <\/strong>This will not work for SHIR and only works when the runtime is Azure integrated runtime for the Azure Data Factory.<br \/>\n<strong>Option B is correct: <\/strong>Site recovery for Azure VM is the best option. When a region failure occurs, we can quickly do a failover to the next region.<br \/>\n<strong>Option C is incorrect: <\/strong>Azure will not replicate data of Azure VMs by default to paired regions or do an automatic failover. We have to set up an Azure Site Recovery service.<br \/>\n<strong>Option D is incorrect<\/strong>: Similar to option C, automatic failover is not an option for Azure VM, unless it is configured by Site Recovery. So, it is not the best answer.<\/p>\n<p><strong>Reference:<\/strong> To know more, please refer to the docs below: <a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/data-factory\/concepts-data-redundancy<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-10\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q21 : Which one of the following T-SQL commands is useful to check the disk space usage, data skew used for tables in the SQL data warehouse database?<\/em><\/h4>\n<p><strong>A. <\/strong>DBCC PDW_SHOWSPACEUSED<br \/>\n<strong>B. <\/strong>DBCC PDW_SHOWPARTITIONSTATS<br \/>\n<strong>C. <\/strong>DBCC PDW_SHOWEXECUTIONPLAN<br \/>\n<strong>D. <\/strong>None of the above<\/p>\n<p><strong>Correct Answer: A<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><strong>Option A is correct<\/strong> because the DBCC PDW_SHOWSPACEUSED T-SQL command is used to display the number of rows, disk space used for tables in SQL DW DBs.<br \/>\n<strong>Option B is incorrect <\/strong>because DBCC PDW_SHOW PARTITIONSTATS T-SQL command is to display the number of rows &amp; size of each partition in the SQL DW table.<br \/>\n<strong>Option C is incorrect<\/strong> because DBCC PDW_SHOWEXECUTIONPLAN T-SQL command is used to provide SQL server query execution plans in Synapse analytics \/ PDW.<\/p>\n<p><strong>References:<\/strong><br \/>\n<a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/t-sql\/database-console-commands\/dbcc-pdw showexecutionplan-transact-sql?view=azure-sqldw-latest<\/a><br \/>\n<a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/t-sql\/database-console-commands\/dbcc-pdw-showpartitionstats transact-sql?view=azure-sqldw-latest<\/a><br \/>\n<a href=\"https:\/\/azure.microsoft.com\/en-in\/resources\/videos\/row-level-security-in-azure-sql-database\/\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/sql\/t-sql\/database-console-commands\/dbcc-pdw-showspaceused transact-sql?toc=\/azure\/synapse-analytics\/sql-data-warehouse\/toc.json&amp;bc=\/azure\/synapse analytics\/sql-data-warehouse\/breadcrumb\/toc.json&amp;view=azure-sqldw-latest&amp;preserve-view=true<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-6\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q22 : Bryan is executing an init script which is required to run a bootstrap script during the Databricks Spark driver or worker node startup.<\/em><br \/>\n<em>Which kind of init script can he choose?<\/em><\/h4>\n<p><strong>A. <\/strong>Global<br \/>\n<strong>B. <\/strong>Job<br \/>\n<strong>C. <\/strong>Cluster-scoped<br \/>\n<strong>D. <\/strong>None of the above<\/p>\n<p><strong>Correct Answer: C<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><strong>Option A is incorrect <\/strong>because the global type of init script can&#8217;t execute on model serving clusters, and it only works on clusters on the same workspace.<br \/>\n<strong>Option B is incorrect <\/strong>because there\u2019s no init script type as Job in Databricks.<br \/>\n<strong>Option C is correct <\/strong>because the Cluster-scoped init script type works for every Databricks cluster configured.<\/p>\n<p><strong>Reference: <\/strong><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/databricks\/clusters\/init-scripts\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/databricks\/clusters\/init-scripts<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_develop_data_processing-7\"><\/span>Domain : Design and develop data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q23 : The Complex event processing streaming solution which Jeffrey is working on the IoT platform, is a hybrid cloud platform where few data sources are transformed into on-premises Big Data platform. The on-premises data center and Azure services are connected via a virtual network gateway.<\/em><br \/>\n<em>What kind of resources can he choose for this on-premises Big data platform connected to Azure via virtual network gateway, complex data processing and execution UDF jobs on java?<\/em><\/h4>\n<p><strong>A. <\/strong>Spark Structured Streaming \/ Apache Storm<br \/>\n<strong>B. <\/strong>Apache Ignite<br \/>\n<strong>C. <\/strong>Apache Airflow<br \/>\n<strong>D. <\/strong>Apache Kafka<br \/>\n<strong>E. <\/strong>None of the above<\/p>\n<p><strong>Correct Answer: A<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><strong>Option A is correct<\/strong> because Spark Structure Streaming\/ Apache Storm can be used for complex event processing for real-time data streams on-premises.<br \/>\n<strong>Option B is incorrect <\/strong>because Apache ignite can\u2019t help with real-time event processing with UDF jobs processing.<br \/>\n<strong>Option C is incorrect<\/strong> because Apache Airflow is a platform for programmatically authoring, monitoring workflows.<br \/>\n<strong>Option D is incorrect<\/strong> because Apache Kafka is used for publishing and consuming event streams in pub-sub scenarios.<\/p>\n<p><strong>Reference: <\/strong><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/stream-analytics\/streaming-technologies\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/stream-analytics\/streaming-technologies<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Design_and_implement_data_storage-11\"><\/span>Domain : Design and implement data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q24 : Nicole is working on migrating on-premises SQL Server databases to Azure SQL data warehouse\u00a0 (Synapse dedicated SQL pools) tables. The tables of the dedicated SQL pools of Synapse Analytics require partition. She\u2019s designing SQL table partitions for this data migration to the Azure Synapse. The partition of the tables already contains the data, and she\u2019s looking for the most efficient method to split the partitions in the dedicated SQL pool tables.<\/em><br \/>\n<em>What T-SQL statement can she use for splitting partitions that contain data?<\/em><\/h4>\n<p><strong>A. <\/strong>CTAS<br \/>\n<strong>B. <\/strong>CETAS<br \/>\n<strong>C. <\/strong>OPENROWSET<br \/>\n<strong>D. <\/strong>Clustered Columnstore Indexes<\/p>\n<p><strong>Correct Answer: A<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><strong>Option A is correct <\/strong>because the CTAS statement can be used as the most efficient method while splitting the partitions that contain data.<br \/>\n<strong>Option B is incorrect <\/strong>because CETAS is used in the dedicated SQL pool of Synapse for creating External table and data export operations in parallel for Hadoop, Azure Blob Storage, and ADLS Gen2.<br \/>\n<strong>Option C is incorrect<\/strong> because the OPENROWSET function in Synapse SQL reads the content of the file(s) from a data source and returns the content as a set of rows.<br \/>\n<strong>Option D is incorrect<\/strong> because Clustered column store tables offer both the highest level of data compression and the best overall query performance. It doesn\u2019t help in partition splitting.<\/p>\n<p><strong>Reference: <\/strong><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql-data-warehouse\/sql-data-warehouse-tables-partition?context=\/azure\/synapse-analytics\/context\/context\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/synapse-analytics\/sql-data-warehouse\/sql-data-warehouse-tables-partition?context=\/azure\/synapse-analytics\/context\/context<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Monitor_and_optimize_data_storage_and_data_processing-3\"><\/span>Domain : Monitor and optimize data storage and data processing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q25 : Which of the following five kinds of log types can Phil select for storing Databricks Diagnostic logs?<\/em><\/h4>\n<p><strong>A. <\/strong>Secrets<br \/>\n<strong>B. <\/strong>RDP<br \/>\n<strong>C. <\/strong>Network<br \/>\n<strong>D. <\/strong>DBFS<br \/>\n<strong>E. <\/strong>Keys<br \/>\n<strong>F. <\/strong>SQL permissions<br \/>\n<strong>G. <\/strong>Accounts<br \/>\n<strong>H. <\/strong>Spark<br \/>\n<strong>I. <\/strong>Ssh<br \/>\n<strong>J. <\/strong>Ambari<\/p>\n<p><strong>Correct Answers: A, D, F, G and I<\/strong><\/p>\n<p><strong>Explanation:<\/strong><\/p>\n<p><strong>Option A is correct <\/strong>because the Secrets log can be stored as Diagnostics logs for Databricks.<br \/>\n<strong>Option B is incorrect <\/strong>because RDP can\u2019t be used as a diagnostic log type for Databricks.<br \/>\n<strong>Option C is incorrect <\/strong>because network flow logs can\u2019t be stored as diagnostics log types for Databricks.<br \/>\n<strong>Option D is correct <\/strong>because DBFS logs can be stored as databricks diagnostics logs.<br \/>\n<strong>Option E is incorrect <\/strong>because Keys can\u2019t be stored as Databricks diagnostics logs.<br \/>\n<strong>Option F is correct <\/strong>because SQL permissions can be stored as Databricks diagnostic log type.<br \/>\n<strong>Option G is correct<\/strong> because databricks accounts logs can be stored as diagnostic logs.<br \/>\n<strong>Option I is correct<\/strong> because ssh logs can also be stored as Databricks diagnostic log type.<br \/>\n<strong>Option J is incorrect<\/strong> because Ambari logs can\u2019t be stored as Databricks diagnostics log type.<\/p>\n<p><strong>Reference: <\/strong><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/databricks\/administration-guide\/account-settings\/azure-diagnostic-logs\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/docs.microsoft.com\/en-us\/azure\/databricks\/administration-guide\/account-settings\/azure-diagnostic-logs<\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Develop_Data_Processing\"><\/span><b>Domain: Develop Data Processing<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q26 : <strong>A Company has received a new project for migrating data from Amazon S3 to Azure Data Lake Storage Gen2. There is a requirement to create a pipeline with approximate data is less than 10 TB.\u00a0 Which of the following is the more efficient and easier-to-use tool to perform the migration?\u00a0<\/strong><\/em><\/h4>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Copy Data Tool\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B. <\/strong>Configure SSIS\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C. <\/strong>Copy Data using Data Flows\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D. <\/strong>None of the above\u00a0<\/span><\/p>\n<p><b>Correct Answer: A<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>Option A is Correct.<\/b><span style=\"font-weight: 400;\"> If you want to copy a small data volume from AWS S3 to Azure (for example, less than 10 TB), the Azure Data Factory Copy Data tool is more efficient and easier to use.\u00a0<\/span><\/p>\n<p><b>Option B is incorrect.<\/b><span style=\"font-weight: 400;\"> Because SSIS is not used t copy data from AWS s3 to Azure, it&#8217;s used to migrate data from on-premises to the cloud.\u00a0<\/span><\/p>\n<p><b>Option C is incorrect.<\/b><span style=\"font-weight: 400;\"> Copy data using Data Flows are used for transformation.\u202f It is not used for copying\/migrating the data from AWS S3 to Azure.\u00a0<\/span><\/p>\n<p><b>References:<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/data-factory\/solution-template-migration-s3-azure\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Migrate data from Amazon S3 to Azure Data Lake Storage Gen2 &#8211; Azure Data Factory | Microsoft Learn<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/data-factory\/copy-data-tool?tabs=data-factory\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Copy Data tool &#8211; Azure Data Factory &amp; Azure Synapse | Microsoft Learn<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Develop_Data_Processing-2\"><\/span><b>Domain: Develop Data Processing<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em>Q27 : <span style=\"font-weight: 400;\"><strong>Arrange the basic steps for implementing and Designing polyBase ETL for a dedicated SQL pool in the correct order.<\/strong>\u00a0<\/span><\/em><\/h4>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> Load the data into dedicated SQL pool staging tables using PolyBase\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B<\/strong>. Extract the source data into text files\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Insert the data into production tables\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> Transform the data\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E.<\/strong> Prepare the data for loading\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>F.<\/strong> Land the data into Azure Blob storage or Azure Data Lake Store\u00a0<\/span><\/p>\n<p><b>Correct Answer: B, F, E, A, D and C<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Polybase is a technology that accesses external data stored in Azure Blob Storage or Azure Data Lake Storage via T-SQL language. Extract, Load, and Transform (ELT) is a process by which data is extracted from a source system, loaded into a data warehouse, and then transformed.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The basic steps for implementing a PolyBase ELT for a dedicated SQL pool are:\u00a0<\/span><\/p>\n<p><b>B. Extract the source data into text files.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>F. Land the data into Azure Blob storage or Azure Data Lake Store.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>E. Prepare the data for loading.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>A. Load the data into dedicated SQL pool staging tables using PolyBase.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>D. Transform the data.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>C. Insert the data into production tables.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>Reference:<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/synapse-analytics\/sql\/load-data-overview\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Design a PolyBase data loading strategy for dedicated SQL pool &#8211; Azure Synapse Analytics | Microsoft Learn<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Secure_Monitor_and_Optimize_Data_Storage_and_Data_Processing\"><\/span><b>Domain: Secure, Monitor, and Optimize Data Storage and Data Processing<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><em><strong>Q28: You monitor Azure Stream Analytics Job. You need to ensure that the job is having enough streaming units provisioned. You configure monitoring of the Streaming Unit (SU) memory% utilization metrics. Which additional metrics you should monitor?\u00a0<\/strong><\/em><\/h4>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> Function events\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> Late Input Events\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Backlogged Input Events\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> Watermark Delay\u00a0<\/span><\/p>\n<p><b>Correct Answers: C and D<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>Option C is correct.<\/b><span style=\"font-weight: 400;\"> Backlogged Input Events. A number of input events are backlogged.\u00a0<\/span><\/p>\n<p><b>Option D is Correct.<\/b><span style=\"font-weight: 400;\"> Watermark Delay. Maximum watermark delay across all partitions of all outputs in the job.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The SU memory% utilization metric, which ranges from 0% to 100%, describes the memory consumption of your workload.\u00a0 For a streaming job with a minimal footprint, this metric is usually between 10% to 20%. If SU% utilization is high (above 80%), or input events get backlogged (even with a low SU% utilization since it doesn&#8217;t show CPU usage), your workload likely requires more compute resources, which requires you to increase the number of SUs. It&#8217;s best to keep the SU metric below 80% to account for occasional spikes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To react to increased workloads and increase streaming units, consider setting an alert of 80% on the SU Utilization metric. Also, you can use watermark delay and backlogged events metrics to see if there&#8217;s an impact.\u00a0<\/span><\/p>\n<p><b>Option A is Incorrect.<\/b><span style=\"font-weight: 400;\"> Function Events are not used to monitor SU memory %, It&#8217;s used to find a number of failed Azure Machine Learning function calls.\u00a0<\/span><\/p>\n<p><b>Option B is Incorrect.<\/b><span style=\"font-weight: 400;\"> Late Inout Events is not used for monitoring SU memory%, it&#8217;s used to monitor events that arrived later than the configured tolerance window for late arrivals.\u00a0<\/span><\/p>\n<p><b>References:<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/stream-analytics\/stream-analytics-time-handling\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/stream-analytics\/stream-analytics-time-handling<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/stream-analytics\/stream-analytics-job-metrics\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Azure Stream Analytics job metrics | Microsoft Learn<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domian_Design_and_Implement_Data_storage\"><\/span><b>Domian: Design and Implement Data storage<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><strong><em>Q29: Moving the data from a single input dataset to a single output dataset, with a process in between is called Data Lineage for 1:1 operation. [State True or False]<\/em><\/strong><\/h4>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> True\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> False\u00a0<\/span><\/p>\n<p><b>Correct Answer: A<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The common pattern for capturing data lineage is moving data from a single input dataset to a single output dataset, with a process in between.\u00a0<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">source\/input: Customer (SQL Table)\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">sink\/output: Customer1.csv (Azure Blob)\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">process: CopyCustomerInfo1#Customer1.csv (Data Factory Copy activity)\u00a0<\/span><\/li>\n<\/ol>\n<p><b>Reference:<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/purview\/how-to-link-azure-data-factory\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Connect to Azure Data Factory &#8211; Microsoft Purview | Microsoft Learn<\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Secure_Monitor_and_Optimize_Data_Storage_and_Data_Processing-2\"><\/span><b>Domain: Secure, Monitor, and Optimize Data Storage and Data Processing<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><strong><em>Q30: The company want to design an E-commerce data solution, in which you need to prevent unauthorized access to sensitive data information of the customer, which is credit card number and phone number information.\u00a0<\/em><\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">You are asked to recommend a solution that must prevent viewing full information of the sensitive information of the customer.\u202f For example, credit card should be displayed as xxxx-xxxx-xxxx-9876. What of the following method is used to protect viewing sensitive data?\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> Table partitions\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> Column Encryption\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Dynamic data masking\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> Sensitive classifications\u00a0<\/span><\/p>\n<p><b>Correct Answer: C<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><b>Option C is correct.<\/b><span style=\"font-weight: 400;\"> Dynamic data masking, It&#8217;s a policy-based security feature that hides the sensitive data in the result set of a query over designated database fields, while the data in the database is not changed.\u00a0<\/span><\/p>\n<p><b>Option A is incorrect.<\/b><span style=\"font-weight: 400;\"> Table partitions enable you to divide your data into smaller groups of data. It cannot protect sensitive data.\u00a0<\/span><\/p>\n<p><b>Option B is incorrect.<\/b><span style=\"font-weight: 400;\"> Column Encryption is also preventing access to sensitive data, but we cannot view the data as masking the data.\u00a0<\/span><\/p>\n<p><b>Option D is incorrect<\/b><span style=\"font-weight: 400;\">. Sensitive classifications are used to apply security controls and access policies.\u00a0<\/span><\/p>\n<p><b>Reference:<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/azure-sql\/database\/dynamic-data-masking-overview?view=azuresql\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Dynamic data masking &#8211; Azure SQL Database | Microsoft Learn<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Summary\"><\/span>Summary<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">We are hopeful that you are able to understand an outline of the DP-203 exam with this free test. To get a Bird\u2019s eye view on the DP-203 exam, go through Whizlabs Practice tests on our official page, and learn the concepts with elaborate explanations. Preparation is always the key to success. Spend more time on learning through DP-203 free questions and practice tests before attempting the real exams. Keep learning!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Came here and looking for DP-203 exam questions? You have certainly landed on the right page. Whizlabs free practice questions not only give you an evaluation of the exam but going through these help you revise the exam-ready concepts. What do Azure Data Engineers do? Azure Data Engineers enable stakeholders in the understanding of Data via exploration. Using different tools and techniques, they enable the development and maintenance of compliant and secure Data Processing Pipelines. They further help with the storage and production of cleansed and enhanced datasets for analysis, using multiple Azure Data Services and Languages. For Who this [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":81503,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[15],"tags":[4837],"class_list":["post-81406","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-azure","tag-dp-203-exam-questions"],"uagb_featured_image_src":{"full":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions-150x150.png",150,150,true],"medium":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions-300x158.png",300,158,true],"medium_large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"1536x1536":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"2048x2048":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"profile_24":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",24,13,false],"profile_48":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",48,25,false],"profile_96":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",96,50,false],"profile_150":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",150,79,false],"profile_300":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",300,158,false],"tptn_thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions-250x250.png",250,250,true],"web-stories-poster-portrait":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",600,315,false],"web-stories-publisher-logo":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",96,50,false],"web-stories-thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/dp-203-free-questions.png",150,79,false]},"uagb_author_info":{"display_name":"Dharmalingam N","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/dharmalingam\/"},"uagb_comment_info":5,"uagb_excerpt":"Came here and looking for DP-203 exam questions? You have certainly landed on the right page. Whizlabs free practice questions not only give you an evaluation of the exam but going through these help you revise the exam-ready concepts. What do Azure Data Engineers do? Azure Data Engineers enable stakeholders in the understanding of Data&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/81406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=81406"}],"version-history":[{"count":11,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/81406\/revisions"}],"predecessor-version":[{"id":94883,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/81406\/revisions\/94883"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/81503"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=81406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=81406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=81406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}