AWS Certified Data Engineer Associate Certification

Key Highlights of the AWS Data Engineer Certification Course

• 2 Full-Length Mock Exams (120+ Unique AWS Data Engineer Certification Practice Exam Questions)
• Access to Labs
• 24/7 Expertise Support
• Unlimited Access for 2 Years

What are the prerequisites for taking the AWS Certified Data Engineer - Associate Certification Exam?

AWS Certified Data Engineer - Associate Certification Exam is mainly designed for individuals who have the following skills:

• Individuals should have 2–3 years of experience in data engineering is highly recommended

• Having 1–2 years of hands-on experience with AWS services can be beneficial

What are the skills measured in the AWS Certified Data Engineer - Associate exam?

AWS Certified Data Engineer - Associate (DEA-C01) examination assesses an individual's capability to carry out various essential tasks related to data engineering. 

To become a skilled Data Engineer, the following skills and knowledge are of paramount importance.

As per the DEA-C01 guides, the AWS Data Engineer validates the following skills:

• Data Ingestion and Transformation (34%)
• Data Store Management (26%)
• Data Operations and Support (22%)
• Data Security and Governance(18%)

Who should take the AWS Certified Data Engineer - Associate Certification Exam?

AWS Certified Data Engineer - Associate Beta Certification Exam suits for following roles:

• The DEA-C01 exam helps to demonstrate proficiency and knowledge as an IT data engineer, whether working in on-premises or cloud environments,
• Those who specialized in the development of extract, transform, and load (ETL) pipelines
• Individuals working with data lakes for storage
• People who need a deep understanding of data analysis and ensure data quality and maintain consistency.

What you'll learn in the AWS Certified Data Engineer - Associate Certification Exam?

The DEA-C01 exam evaluates an individual's competence in completing the following key responsibilities:

• Ingesting and transforming data and effectively orchestrating data pipelines by applying programming principles.
• Selecting the most suitable data storage solution, designing data models, cataloging data schemas, and managing data lifecycles.
• Operationalizing, maintaining, and closely monitoring data pipelines, along with conducting data analysis and ensuring data quality.
• Implementing appropriate measures for authentication, authorization, data encryption, privacy, and governance, including the enabling of robust logging mechanisms.