AWS Certified Data Engineer Associate DEA-C01 Full Description, Overview and Ide

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The AWS Certified Data Engineer – Associate (DEA-C01) exam validates your ability to design, build, secure, and optimize data pipelines on AWS. It focuses on end-to-end data workflows, from ingesting raw data to transforming, storing, and operationalizing it in scalable and cost-efficient ways. The exam confirms that you understand how to handle batch and streaming data, apply best practices, and maintain reliable data operations in a cloud environment.

EXAM OVERVIEW
The DEA-C01 exam tests practical data engineering skills used in real AWS environments. It covers building data ingestion systems, designing secure storage layers, orchestrating and automating ETL pipelines, monitoring performance, and applying governance controls. The exam uses multiple-choice and multiple-response questions and is designed for professionals with hands-on experience with data engineering tools and AWS services. It focuses on practical decision-making rather than theoretical data-science tasks.

IDEAL FOR
• Data engineers working with cloud-based data pipelines
• Professionals with 2–3 years of data engineering or analytics engineering experience
• Cloud engineers who maintain or optimize data workflows on AWS
• Developers transitioning into data-centric cloud roles
• Teams needing validated skills in data ingestion, transformation, and governance
• Anyone responsible for building or maintaining data lakes, warehouses, or streaming systems

COVERING TOPICS

• Data Ingestion and Transformation
  • Building batch and streaming data pipelines
  • Using ETL and ELT processes
  • Working with workflow automation and distributed data processing
  • Handling schema evolution and data quality
    
    
• Data Storage and Optimization
  • Choosing storage based on performance and cost
  • Designing scalable data lakes and warehouses
  • Using partitioning, indexing, compression, and lifecycle strategies
  • Ensuring efficient query performance and long-term data durability
    
    
• Data Operations and Orchestration
  • Automating pipelines and scheduling workflows
  • Monitoring data jobs, resolving failures, and optimizing runtime
  • Managing pipeline reliability, observability, and error handling
    
    
• Security, Compliance, and Governance
  • Implementing access control and permissions
  • Encrypting data in transit and at rest
  • Applying governance rules, auditing, and metadata management
  • Ensuring compliance with organizational security standards
    
    
• What’s Not Included
  • Machine learning model development
  • BI dashboard creation
  • General software application programming
  • Deep data-science algorithms