Free AWS DEA-C01 Exam Questions

Core Data Engineering Skills:

• Data Ingestion - Streaming data (Kinesis, MSK, DynamoDB Streams, DMS) and batch data (S3, Glue, EMR, Lambda, AppFlow) ingestion patterns, throughput and latency characteristics, replayability, stateful and stateless transactions
• Data Transformation - ETL pipeline creation, processing structured and unstructured data using Apache Spark, optimizing container usage (EKS, ECS), transforming data between formats (CSV to Parquet), troubleshooting transformation failures
• Data Orchestration - Building workflows using Lambda, EventBridge, MWAA, Step Functions, Glue workflows, implementing serverless workflows, event-driven architecture, scheduling and dependency management
• Programming Concepts - CI/CD for data pipelines, SQL query optimization, Infrastructure as Code (CDK, CloudFormation), distributed computing, data structures and algorithms, Git commands, AWS SAM for serverless deployments
• Data Store Management - Choosing appropriate storage services (Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis, MSK, S3), configuring for performance and cost, data migration tools (Transfer Family, Redshift federated queries, Spectrum)
• Data Cataloging - Building and referencing data catalogs (Glue Data Catalog, Hive metastore), schema discovery with Glue crawlers, synchronizing partitions, creating source/target connections
• Data Lifecycle Management - S3 Lifecycle policies, storage tier optimization, data retention and archiving, S3 versioning, DynamoDB TTL, load/unload operations between S3 and Redshift
• Data Modeling - Schema design for Redshift, DynamoDB, and Lake Formation, indexing and partitioning strategies, compression, schema evolution, data lineage tracking
• Data Operations - Automating data processing, orchestrating pipelines (MWAA, Step Functions), troubleshooting managed workflows, consuming and maintaining data APIs, querying data with Athena
• Data Analysis - Visualizing data (Glue DataBrew, QuickSight), verifying and cleaning data, SQL queries with JOIN clauses, data aggregation and pivoting, using Athena notebooks with Spark
• Monitoring and Maintenance - Logging application data, CloudWatch Logs, CloudTrail for API tracking, troubleshooting performance issues, analyzing logs with Athena, EMR, OpenSearch, CloudWatch Logs Insights
• Data Quality - Data quality checks, defining quality rules (Glue DataBrew), data sampling, data validation (completeness, consistency, accuracy, integrity), investigating data consistency
• Security and Governance - Authentication (IAM, VPC security groups, Secrets Manager), authorization (IAM policies, Lake Formation permissions), data encryption (KMS, client-side and server-side encryption), data masking and anonymization
• Audit and Compliance - CloudTrail for API tracking, CloudWatch Logs for application logs, CloudTrail Lake for centralized logging, PII identification (Macie with Lake Formation), data sovereignty, AWS Config for configuration changes

Exam Sections (4 Main Domains with Weightings):

1. Domain 1: Data Ingestion and Transformation (34%) - Perform data ingestion from streaming and batch sources, transform and process data using ETL pipelines, orchestrate data pipelines with AWS services, apply programming concepts including CI/CD, SQL optimization, IaC, and distributed computing
2. Domain 2: Data Store Management (26%) - Choose appropriate data stores for cost and performance requirements, understand data cataloging systems (Glue Data Catalog, Hive metastore), manage data lifecycle with S3 Lifecycle policies and versioning, design data models and handle schema evolution
3. Domain 3: Data Operations and Support (22%) - Automate data processing using AWS services, analyze data with SQL queries and visualization tools, maintain and monitor data pipelines with CloudWatch and CloudTrail, ensure data quality through validation and profiling
4. Domain 4: Data Security and Governance (18%) - Apply authentication mechanisms (IAM, VPC security, Secrets Manager), apply authorization mechanisms (IAM policies, Lake Formation), ensure data encryption and masking, prepare logs for audit, understand data privacy and governance including PII protection and data sovereignty

Key AWS Services Covered:

• Analytics: Athena, EMR, Glue, Glue DataBrew, Lake Formation, Kinesis Data Firehose, Kinesis Data Streams, Managed Service for Apache Flink, MSK, OpenSearch Service, QuickSight
• Application Integration: AppFlow, EventBridge, MWAA, SNS, SQS, Step Functions
• Compute: Batch, EC2, Lambda, AWS SAM
• Containers: ECR, ECS, EKS
• Database: DocumentDB, DynamoDB, Keyspaces, MemoryDB, Neptune, RDS, Redshift
• Migration: DMS, DataSync, Schema Conversion Tool, Transfer Family
• Storage: S3, S3 Glacier, EBS, EFS, AWS Backup
• Security: IAM, KMS, Macie, Secrets Manager, PrivateLink
• Management: CloudFormation, CloudTrail, CloudWatch, CloudWatch Logs, Config, Systems Manager