AWS Certified Data Engineer – Associate Practise Exam

Who it is for

• Data engineers preparing for the AWS Certified Data Engineer – Associate Certification (DEA-C01).
• Practitioners who need practice implementing, monitoring, troubleshooting, and securing AWS data pipelines.

What this exam covers

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

What you will practice

• Ingest streaming and batch data with Amazon Kinesis, Amazon MSK, Amazon S3, and AWS Glue, transform it with AWS Glue and Amazon EMR, and orchestrate pipelines with AWS Step Functions and Amazon MWAA.
• Choose Amazon Redshift, Amazon DynamoDB, Amazon RDS, Amazon Aurora, and AWS Lake Formation stores, catalog schemas with AWS Glue, and manage Amazon S3 Lifecycle and DynamoDB TTL.
• Automate and query pipelines with Amazon Athena, AWS Glue DataBrew, and Amazon EventBridge, monitor with Amazon CloudWatch and AWS CloudTrail, and apply data quality rules.
• Apply IAM and AWS Lake Formation permissions, encrypt data with AWS KMS, and identify PII with Amazon Macie.

About this practice exam

The AWS Certified Data Engineer – Associate exam tests a candidate’s ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices: ingest and transform data and orchestrate data pipelines while applying programming concepts; choose an optimal data store, design data models, catalog data schemas, and manage data lifecycles; operationalize, maintain, and monitor data pipelines, analyze data, and ensure data quality; and implement authentication, authorization, data encryption, privacy, and governance. This practice exam prepares you for AWS Certified Data Engineer – Associate (DEA-C01) across Data Ingestion and Transformation, Data Store Management, Data Operations and Support, and Data Security and Governance.

The exam emphasizes streaming and batch ingestion with Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon S3, AWS Glue, Amazon EMR, and AWS Database Migration Service (AWS DMS); transformation and orchestration with AWS Glue, Amazon EMR, Amazon Redshift, AWS Lambda, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow (Amazon MWAA); data stores and catalogs including Amazon Redshift, Amazon DynamoDB, Amazon RDS, Amazon Aurora, AWS Lake Formation, and the AWS Glue Data Catalog; lifecycle management with Amazon S3 Lifecycle policies and Amazon DynamoDB time to live (TTL); analysis and quality with Amazon Athena, AWS Glue DataBrew, and Amazon QuickSight; and security with AWS Identity and Access Management (IAM), AWS Lake Formation, AWS Key Management Service (AWS KMS), and Amazon Macie. It does not primarily assess performing machine learning training and inferences, programming language-specific syntax, or drawing business conclusions based on data.

The official AWS Certified Data Engineer – Associate (DEA-C01) exam is 65 questions in 130 minutes. This practice exam follows the four official Data Engineer – Associate domains and their published weights.

The certification validates understanding of:

• Data Ingestion and Transformation: streaming and batch sources including Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon MSK, Amazon DynamoDB Streams, Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, and Amazon AppFlow; transformation with AWS Glue, Amazon EMR, Lambda, and Amazon Redshift including format conversion such as CSV to Apache Parquet; orchestration with Lambda, Amazon EventBridge, Amazon MWAA, AWS Step Functions, and AWS Glue workflows; notifications with Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Service (Amazon SQS); and programming concepts including SQL, Infrastructure as Code (IaC) with AWS CloudFormation and AWS Cloud Development Kit (AWS CDK), and AWS Serverless Application Model (AWS SAM).
• Data Store Management: storage services including Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, Amazon Aurora, Amazon MemoryDB, Amazon Kinesis Data Streams, and Amazon MSK; open table formats such as Apache Iceberg; catalogs with AWS Glue Data Catalog, AWS Glue crawlers, and Amazon SageMaker Catalog; data lifecycle with Amazon S3 Lifecycle policies, Amazon S3 versioning, and DynamoDB TTL; schema design for Amazon Redshift, DynamoDB, and Lake Formation; and schema conversion with AWS DMS.
• Data Operations and Support: automation with Amazon MWAA, AWS Step Functions, AWS Glue, Amazon EMR, Amazon Redshift, AWS Lambda, Amazon EventBridge, Amazon Athena, AWS Glue DataBrew, and Amazon SageMaker Unified Studio; analysis with Amazon QuickSight, Amazon Athena, and SQL on Amazon Redshift; pipeline monitoring with AWS CloudTrail, Amazon CloudWatch Logs, Amazon OpenSearch Service, AWS Glue, and Amazon EMR; and data quality checks and rules with AWS Glue DataBrew.
• Data Security and Governance: authentication with IAM roles and policies, Amazon VPC security groups, and AWS Secrets Manager; authorization with AWS Lake Formation, Amazon Redshift, and least-privilege custom IAM policies; encryption and masking with AWS KMS; audit logging with AWS CloudTrail, Amazon CloudWatch Logs, AWS CloudTrail Lake, Amazon Athena, and Amazon OpenSearch Service; and privacy and governance including personally identifiable information (PII) identification with Amazon Macie and Lake Formation, AWS Config, data sovereignty, and Amazon SageMaker Catalog projects.