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AWS Certified Data Engineer - Associate (DEA-C01) Syllabus

Data Ingestion and Transformation

Ingesting batch and streaming data, transforming it, orchestrating pipelines and applying programming concepts.

34%
Weight
22
Questions
34
Marks

Data Ingestion

  • Streaming ingestion with Amazon Kinesis, Amazon MSK and DynamoDB Streams
  • Batch ingestion with Amazon S3, AWS Glue, AWS DMS and Amazon AppFlow
  • Scheduling and event triggers with Amazon EventBridge
  • Throttling, rate limits and fan-in/fan-out
  • Replayability and stateful vs. stateless processing

Transformation and Processing

  • Choosing AWS Glue, Amazon EMR, AWS Lambda or Amazon Redshift
  • Format conversion such as CSV to Apache Parquet
  • JDBC/ODBC connections and multi-source integration
  • Optimizing processing cost
  • Troubleshooting transformation failures

Pipeline Orchestration

  • AWS Step Functions and Amazon MWAA
  • AWS Glue workflows and EventBridge
  • Serverless workflow design
  • Alerting with Amazon SNS and Amazon SQS
  • Resiliency and fault tolerance

Programming Concepts

  • Python and SQL for data pipelines
  • Lambda concurrency and performance tuning
  • Infrastructure as code with CloudFormation, CDK and AWS SAM
  • CI/CD and version control for pipelines
  • Distributed computing concepts

Data Store Management

Choosing data stores, cataloging data, managing data lifecycle and designing data models and schema evolution.

26%
Weight
17
Questions
26
Marks

Choosing a Data Store

  • Amazon Redshift, Amazon RDS and Amazon DynamoDB selection
  • Access patterns and cost/performance trade-offs
  • Redshift Spectrum, federated queries and materialized views
  • Apache Iceberg open table format
  • Vector index types (HNSW, IVF)

Data Cataloging

  • AWS Glue Data Catalog and Hive metastore
  • AWS Glue crawlers for schema discovery
  • Partition synchronization
  • Amazon SageMaker Catalog for business metadata

Data Lifecycle Management

  • Loading and unloading between S3 and Redshift
  • S3 Lifecycle policies and storage classes
  • S3 Versioning and DynamoDB TTL
  • Deleting data for legal requirements

Data Modeling and Schema Evolution

  • Schema design for Redshift and DynamoDB
  • Schema conversion with AWS SCT and DMS Schema Conversion
  • Partitioning, indexing and compression
  • Data lineage tracking
  • Handling schema changes

Data Operations and Support

Automating data processing, analyzing data, maintaining and monitoring pipelines and ensuring data quality.

22%
Weight
14
Questions
22
Marks

Automating Data Processing

  • Orchestration with Amazon MWAA and Step Functions
  • Querying data with Amazon Athena
  • Data preparation with AWS Glue DataBrew and SageMaker Unified Studio
  • Calling AWS SDKs from code

Analyzing Data

  • SQL queries and views in Redshift and Athena
  • Visualization with Amazon QuickSight
  • Athena notebooks with Apache Spark
  • Provisioned vs. serverless trade-offs
  • Aggregations, rolling averages and pivoting

Monitoring and Data Quality

  • Amazon CloudWatch Logs and alarms
  • AWS CloudTrail for API tracking
  • Troubleshooting Glue and EMR pipelines
  • Data quality rules with DataBrew
  • Sampling and data skew

Data Security and Governance

Applying authentication, authorization, encryption, audit logging and data privacy and governance controls.

18%
Weight
12
Questions
18
Marks

Authentication and Authorization

  • IAM roles, policies and least privilege
  • AWS Secrets Manager and Parameter Store
  • AWS Lake Formation permissions
  • Role-, tag- and attribute-based access control
  • Redshift database users, groups and roles

Encryption and Masking

  • AWS KMS encryption and key policies
  • Cross-account encryption
  • Encryption in transit
  • Data masking and anonymization

Audit Logging, Privacy and Governance

  • CloudTrail Lake for centralized log queries
  • PII discovery with Amazon Macie
  • Redshift data sharing
  • AWS Config for configuration changes
  • Data sovereignty and Region restrictions

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Learning Progress

Data Ingestion and Transformation 85%
Data Store Management 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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