This guide covers all official exam domains for the AWS Certified Data Engineer Associate certification, focusing on data store management, operations, security, governance, and ingestion/transformation.
This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.
18 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.
Start Chapter 1Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.
Go to practice testEvery DEA-C01term defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.
Browse glossaryExam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.
View exam guideAWS Data Engineering Overview and Core Concepts
Objective 1.1 · Explain the value and purpose of data engineering in AWS
Introduction to AWS Data Stores: Relational, Key-Value, Document, and Analytics
Objective 1.1 · Identify appropriate data stores based on data characteristics and access patterns
Amazon S3 as a Data Lake Store: Buckets, Objects, and Lifecycle Policies
Objective 1.2 · Design and implement data storage solutions using Amazon S3
Amazon RDS and Aurora: Managed Relational Databases for Data Engineering
Objective 1.3 · Deploy and manage relational database instances on AWS
Amazon DynamoDB: NoSQL Key-Value and Document Database for High-Throughput Workloads
Objective 1.4 · Implement NoSQL data stores for scalable applications
Amazon Redshift: Cloud Data Warehousing and Analytics
Objective 1.5 · Set up and optimize data warehousing solutions using Amazon Redshift
Data Pipeline Orchestration with AWS Step Functions and AWS Glue Workflows
Objective 2.1 · Design and implement data workflows and orchestration
Data Quality, Monitoring, and Alerting with AWS Services
Objective 2.2 · Monitor data pipeline operations and implement data quality checks
Automating Data Operations: AWS Lambda, EventBridge, and CloudWatch
Objective 2.3 · Automate data operations and respond to events
IAM Policies and Data Access Control for AWS Data Services
Objective 3.1 · Implement identity and access management for data stores
Data Encryption at Rest and in Transit with AWS KMS and ACM
Objective 3.2 · Configure encryption for data at rest and in transit
Data Governance, Logging, and Compliance with AWS CloudTrail and Config
Objective 3.3 · Implement data governance and audit controls
AWS Glue: Serverless ETL and Data Catalog for Data Preparation
Objective 4.1 · Design and implement ETL pipelines using AWS Glue
Amazon Kinesis: Real-Time Data Streaming and Ingestion
Objective 4.2 · Ingest and process streaming data with Amazon Kinesis
Data Ingestion into AWS: S3 Transfer, AWS DataSync, and Snow Family
Objective 4.3 · Ingest data from various sources using AWS transfer services
Amazon EMR and Apache Spark: Distributed Data Processing at Scale
Objective 4.4 · Process large-scale datasets using Amazon EMR and Spark
Data Transformation and Querying with AWS Glue Studio and Amazon Athena
Objective 4.5 · Transform and query data using serverless services
Capstone: Building an End-to-End Data Ingestion and Transformation Pipeline
Objective 4.6 · Integrate multiple AWS services to build a complete data pipeline
Free DEA-C01 practice questions with full explanations. Test what you learn chapter by chapter.
DEA-C01 Practice Questions