AWS Certified Data Engineer — Associate (DEA-C01) Study Guide [165 Minute Read]

A First-Principles Approach to Data Engineering on AWS

Welcome to the AWS Certified Data Engineer — Associate (DEA-C01) Study Guide. This guide moves beyond surface-level memorization. It is designed to build a robust mental model of how data pipelines work on AWS — understanding the why behind every architectural decision, service selection, and optimization trade-off.

Each topic is aligned with the official DEA-C01 Exam Objectives (v1.1), targeting the specific cognitive skills required for success. The exam is heavily scenario-based — roughly 60% of questions present a business situation and ask you to choose the best AWS service or architectural approach. Rote memorization of service features will not get you through; you need to understand when and why each service is the right tool.

Exam Details: 65 questions (50 scored + 15 unscored) | 130 minutes | Passing score: 720/1000

Prerequisites: 2–3 years of data engineering experience. 1–2 years of hands-on AWS. Familiarity with ETL/ELT pipelines, SQL, data lakes, Git, and general networking/storage/compute concepts. No ML training or programming language-specific syntax is tested.

Exam Domain Weights

Domain 1 commands over a third of the exam — ingestion patterns, transformation services (especially AWS Glue and EMR), and pipeline orchestration are your highest-value study areas. Domain 2's data store selection questions are the second-heaviest block, so invest deeply in understanding when to choose S3, Redshift, DynamoDB, or Aurora. Domains 3 and 4 together account for 40%, with monitoring, data quality, IAM, Lake Formation, and encryption rounding out the exam.

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(Table of Contents - For Reference)

• Phase 1: First Principles of Data Engineering on AWS
  • 1.1. The Data Pipeline Mental Model
    • 1.1.1. Why Data Pipelines Exist: The Source-to-Insight Problem
    • 1.1.2. The Five Stages of a Data Pipeline
    • 1.1.3. Batch vs Streaming: Two Paradigms for Data Movement
  • 1.2. The AWS Data Engineering Landscape
    • 1.2.1. Core Service Categories and When They Apply
    • 1.2.2. Managed vs Serverless: The Operational Trade-Off
  • 1.3. Thinking Like a Data Engineer
    • 1.3.1. The Three Optimization Axes: Cost, Performance, and Reliability
    • 1.3.2. Schema-on-Read vs Schema-on-Write: A Foundational Decision
  • 1.4. Reflection Checkpoint
• Phase 2: Data Ingestion and Transformation (34%)
  • 2.1. Streaming Data Ingestion
    • 2.1.1. Amazon Kinesis Data Streams
    • 2.1.2. Amazon Kinesis Data Firehose
    • 2.1.3. Amazon MSK (Managed Streaming for Apache Kafka)
    • 2.1.4. Change Data Capture: DynamoDB Streams and AWS DMS
  • 2.2. Batch Data Ingestion
    • 2.2.1. Amazon S3 as an Ingestion Layer
    • 2.2.2. AWS Glue Crawlers and Batch Jobs
    • 2.2.3. AWS Database Migration Service and AppFlow
  • 2.3. Scheduling, Triggers, and Ingestion Patterns
    • 2.3.1. Event-Driven Ingestion with EventBridge
    • 2.3.2. Scheduled Ingestion with MWAA and Glue Triggers
    • 2.3.3. Throttling, Rate Limits, and Fan-Out Patterns
  • 2.4. Data Transformation Services
    • 2.4.1. AWS Glue ETL: DynamicFrames, Spark, and Job Bookmarks
    • 2.4.2. Amazon EMR and Apache Spark
    • 2.4.3. Lightweight Transforms with Lambda and Redshift SQL
  • 2.5. Data Formats and Conversion
    • 2.5.1. Columnar vs Row-Based Formats
    • 2.5.2. Format Selection and Conversion Patterns
  • 2.6. Pipeline Orchestration
    • 2.6.1. AWS Step Functions and State Machines
    • 2.6.2. Amazon MWAA (Apache Airflow) and Glue Workflows
    • 2.6.3. Event-Driven Architectures with SNS and SQS
  • 2.7. Programming Concepts for Data Pipelines
    • 2.7.1. SQL for Data Transformation and Query Optimization
    • 2.7.2. CI/CD and Infrastructure as Code
    • 2.7.3. Serverless Deployment with Lambda and SAM
  • 2.8. Reflection Checkpoint
• Phase 3: Data Store Management (26%)
  • 3.1. Choosing the Right Data Store
    • 3.1.1. Amazon S3: The Data Lake Foundation
    • 3.1.2. Amazon Redshift: The Cloud Data Warehouse
    • 3.1.3. Amazon DynamoDB: NoSQL at Scale
    • 3.1.4. Amazon RDS, Aurora, and Relational Options
  • 3.2. Specialized and Emerging Data Stores
    • 3.2.1. Amazon OpenSearch, Neptune, DocumentDB, Keyspaces, and MemoryDB
    • 3.2.2. Open Table Formats: Apache Iceberg and S3 Tables
    • 3.2.3. Vector Indexes and Vectorization Concepts
  • 3.3. Data Cataloging Systems
    • 3.3.1. AWS Glue Data Catalog and Crawlers
    • 3.3.2. Business Data Catalogs: Amazon SageMaker Catalog
  • 3.4. Data Lifecycle Management
    • 3.4.1. S3 Storage Classes and Lifecycle Policies
    • 3.4.2. Data Retention, Archiving, and Deletion Strategies
  • 3.5. Data Modeling and Schema Evolution
    • 3.5.1. Schema Design for Redshift, DynamoDB, and Lake Formation
    • 3.5.2. Schema Evolution, Data Lineage, and Data Quality
  • 3.6. Reflection Checkpoint
• Phase 4: Data Operations and Support (22%)
  • 4.1. Automating Data Processing
    • 4.1.1. Orchestration with Step Functions and MWAA
    • 4.1.2. Processing with Glue, EMR, Lambda, and Athena
  • 4.2. Data Analysis on AWS
    • 4.2.1. SQL Analysis with Athena and Redshift
    • 4.2.2. Visualization and Exploration: QuickSight, DataBrew, and Notebooks
  • 4.3. Monitoring and Maintaining Pipelines
    • 4.3.1. CloudWatch Metrics, Logs, and Alarms
    • 4.3.2. CloudTrail, CloudTrail Lake, and Log Analysis
    • 4.3.3. Troubleshooting Pipeline Failures
  • 4.4. Ensuring Data Quality
    • 4.4.1. Validation, Profiling, and Quality Rules
    • 4.4.2. Data Sampling and Skew Mechanisms
  • 4.5. Reflection Checkpoint
• Phase 5: Data Security and Governance (18%)
  • 5.1. Authentication on AWS
    • 5.1.1. IAM Fundamentals: Users, Roles, and Policies
    • 5.1.2. VPC Security, Endpoints, and Credential Management
  • 5.2. Authorization and Access Control
    • 5.2.1. IAM Policies, RBAC, and Attribute-Based Access
    • 5.2.2. Lake Formation Permissions and Fine-Grained Access
  • 5.3. Data Encryption and Masking
    • 5.3.1. Encryption at Rest and in Transit with KMS
    • 5.3.2. Data Masking, Anonymization, and PII Protection
  • 5.4. Audit Logging
    • 5.4.1. CloudTrail and CloudWatch Logs for Auditing
    • 5.4.2. Centralized Log Analysis: CloudTrail Lake, Athena, and OpenSearch
  • 5.5. Data Privacy and Governance
    • 5.5.1. PII Detection with Macie and Data Sovereignty
    • 5.5.2. Governance Frameworks: Config, SageMaker Catalog, and Data Sharing
  • 5.6. Reflection Checkpoint
• Phase 6: Exam Readiness
  • 6.1. Exam Strategy and Time Management
  • 6.2. Quick Reference: Decision Trees and Service Comparisons
  • 6.3. Mixed-Topic Practice Questions
• Phase 7: Glossary
• Phase 8: Conclusion

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