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DEA-C01

AWS Certified Data Engineer - Associate (DEA-C01)

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1 Data ingestion and transformationStreaming and batch ingestion, transformation, orchestration, and programming.112questionsHigh
2 Data store managementData stores, catalogs, lifecycles, data models, and schema evolution.82questionsHigh
3 Data operations and supportData processing automation, analysis, monitoring, maintenance, and quality.69questionsHigh
4 Data security and governanceAuthentication, authorization, encryption, masking, auditing, and privacy.59questionsHigh
4 areas / 17 objectives selected322questionseligible

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1.1 Ingest data30 questions
  1. Replay an ordered event stream for multiple consumers
  2. Deliver a stream to S3 with low operational overhead
  3. Continuously copy RDS changes into an S3 data lake
  4. Process DynamoDB item changes as events
  5. Move an existing Kafka event platform to AWS
  6. Avoid a hot shard in Kinesis Data Streams
  7. Separate low-latency reads for several stream consumers
  8. Load SaaS records into S3 on a schedule
  9. Replay retained stream data from the beginning
  10. Aggregate stream events in a time window
  11. Scale Kinesis shard capacity before a traffic peak
  12. Automatically follow highly variable stream traffic
  13. Deliver records to S3 prefixes by an attribute
  14. Transform records before Firehose delivery
  15. Replicate large LOB columns completely with DMS
  16. Resume a DMS CDC task from its change position
  17. Retain before-and-after DynamoDB images for audit
  18. Preserve ordering for one DynamoDB item while scaling
  19. Ingest external database changes into Kafka
  20. Simplify operations for intermittent Kafka traffic
  21. Send small events to Kinesis efficiently
  22. Process several JSON objects as separate Firehose records
  23. Import only changed SaaS records on a schedule
  24. Extend stream retention for an investigation
  25. Let several processors independently read the same events
  26. Keep Kafka ingestion available during a broker failure
  27. Isolate throughput for multiple stream consumers
  28. Ingest a paginated data API with restartable progress
  29. Use a fixed source IP to connect to an external data source
  30. Invoke Lambda from Kinesis records
1.2 Transform and process data30 questions
  1. Convert CSV data to analysis-friendly Parquet
  2. Normalize drifting input schemas in Glue
  3. Transform only new files in a recurring Glue job
  4. Run a large distributed Spark transformation
  5. Normalize small events with Lambda
  6. Bulk-load Parquet data from S3 into Redshift
  7. Export Redshift aggregates to a data lake
  8. Place transformed data in date partitions
  9. Choose compression for analytical Parquet data
  10. Reduce Spark delay caused by data skew
  11. Select a Glue job for a small Python transformation
  12. Handle missing and invalid values during ETL
  13. Aggregate a stream with stateful time windows
  14. Run intermittent Spark ETL without a persistent cluster
  15. Join customer and order data in Glue
  16. Remove duplicate events in ETL
  17. Normalize timestamps from multiple regions to UTC
  18. Flatten nested JSON into analytical columns
  19. Design a Redshift sort order for analytical queries
  20. Parse CSV quoting and delimiters correctly
  21. Mask sensitive columns during ETL
  22. Evolve a table while preserving newly added columns
  23. Align aggregation grain to prevent double counting
  24. Create searchable chunks for a knowledge base
  25. Keep the latest event with Spark
  26. Make transformation errors replayable
  27. Run a large ETL job with dependent libraries in containers
  28. Publish aggregate data safely to other systems through an API
  29. Choose processing methods based on data volume, speed, and format
  30. Connect a business database to a Glue ETL job with JDBC
1.3 Orchestrate data pipelines25 questions
  1. Run ETL jobs in dependency order
  2. Start a daily pipeline at a specified time
  3. Retry only transient ETL failures
  4. Manage dependent tasks with a DAG
  5. Chain a Glue crawler and ETL job
  6. Start a pipeline from an S3 arrival event
  7. Run file-level ETL in parallel
  8. Resume a pipeline after external approval
  9. Backfill only a missing date in Airflow
  10. Route pipeline failures to an operations notification
  11. Wait until a specified time in a workflow
  12. Run independent quality checks in parallel
  13. Process a very large file set with Distributed Map
  14. Pass only selected queue events to processing
  15. Wait for an S3 arrival before an Airflow task
  16. Publish only after a Glue quality check succeeds
  17. Choose a workflow type with long-running history
  18. Normalize events before passing them to a workflow
  19. Avoid duplicate publication when a task is retried
  20. Make failed event delivery replayable
  21. Return an external worker result to a workflow
  22. Replay missed events after correcting a rule
  23. Limit concurrent calls to an external API in Airflow
  24. Reuse one Glue workflow with a date parameter
  25. Switch workflow definitions without changing running executions
1.4 Apply programming concepts27 questions
  1. Count events by key in one pass
  2. Select the latest row for each customer
  3. Detect a failure in a Bash pipeline
  4. Reuse one data platform across environments
  5. Build a reusable data-platform component with CDK
  6. Deploy an S3-triggered transformation with SAM
  7. Promote ETL code only after tests pass
  8. Distribute ingestion keys while preserving entity order
  9. Process a large input incrementally in Python
  10. Pass input safely to a SQL query
  11. Reuse a filtered intermediate result in multiple aggregates
  12. Validate and isolate invalid numeric input
  13. Build a Spark transformation without shared mutable state
  14. Aggregate records containing NULL values correctly
  15. Pass JSON arguments safely from Bash
  16. Represent CloudFormation resource dependencies safely
  17. Make CDK synthesis reproducible across environments
  18. Automatically test transformation logic with small fixtures
  19. Join a small master dataset efficiently in Spark
  20. Serialize Python timestamps in a consistent format
  21. Identify the cause of a slow transformation query
  22. Remove temporary files on every Bash exit path
  23. Resume Spark Streaming from a checkpoint
  24. Detect a breaking schema change before deployment
  25. Compute the top K results over distributed data
  26. Use a large shared library from a Lambda transformation
  27. Tune Lambda concurrency and transformation performance
2.1 Choose appropriate data stores23 questions
  1. Store large raw data at low cost
  2. Provide millisecond key-based access
  3. Accelerate large aggregation queries
  4. Store transactional business data with strict consistency
  5. Search logs by text and time range
  6. Process events with sub-second latency
  7. Keep frequently read state in memory
  8. Govern integrated access to a data lake
  9. Separate analytics load from a production RDS database
  10. Separate large files from operational metadata
  11. Model multiple entities for key-based access
  12. Query DynamoDB with a different search key
  13. Choose a relational database for high write volume
  14. Run occasional SQL analysis on stored S3 data
  15. Join Redshift tables with data kept in S3
  16. Perform approximate nearest-neighbor vector search
  17. Choose durable Redis-compatible state
  18. Buffer and deliver events automatically to a destination
  19. Access temporary analytical data with low latency
  20. Query high-volume time-series events by key
  21. Ingest a partner's SFTP files into data processing
  22. Analyze current data in an external RDS database
  23. Investigate Redshift table-lock contention
2.2 Manage data catalogs21 questions
  1. Automatically register S3 schemas
  2. Exclude temporary S3 prefixes from a crawler
  3. Expose a new date partition in the catalog
  4. Manage schema-change history
  5. Manage compatible schemas for streaming events
  6. Share table definitions across EMR engines
  7. Balance crawler frequency and cost
  8. Make data assets discoverable across teams
  9. Share a catalog across AWS accounts
  10. Attach ownership and classification metadata to a table
  11. Query millions of time partitions without registering each one
  12. Control crawler schema changes
  13. Classify a proprietary log format with a crawler
  14. Expose a shared catalog table in an analytics account
  15. Accelerate catalog searches over many partitions
  16. Manage the table location precisely
  17. Use column statistics to optimize analysis
  18. Check compatibility when registering a new event schema
  19. Prevent accidental catalog table deletion
  20. Manage catalog permissions with classification tags
  21. Manage schema changes in a shared data catalog
2.3 Manage data lifecycle19 questions
  1. Transition S3 storage classes as access declines
  2. Recover an accidentally overwritten S3 object
  3. Automatically delete expired DynamoDB sessions
  4. Load only selected files into Redshift
  5. Export Redshift data to date-partitioned S3
  6. Restore RDS just before an accidental update
  7. Prevent deletion during a legal retention period
  8. Replicate a data lake to another Region
  9. Create an on-demand DynamoDB backup
  10. Restore archive data before analysis
  11. Expire noncurrent S3 versions after retention
  12. Back up a Redshift cluster daily
  13. Restore DynamoDB to a recent point in time
  14. Export DynamoDB data to an analytical S3 lake
  15. Set the RDS automated-backup retention window
  16. Propagate source deletes to a replica
  17. Copy Redshift backups to another Region
  18. Manage unpredictable S3 access patterns
  19. Protect backups from administrator deletion
2.4 Design data models and schema evolution19 questions
  1. Reduce data redistribution in a large Redshift join
  2. Reduce scans for time-range queries
  3. Choose Redshift column compression
  4. Model DynamoDB access patterns for orders
  5. Add a new DynamoDB search condition
  6. Preserve backward compatibility for stream schemas
  7. Migrate an on-premises database schema to AWS
  8. Track data origin and transformation history
  9. Add a column to an Iceberg analytical table
  10. Group related customer items in DynamoDB
  11. Search only qualifying items with a DynamoDB GSI
  12. Adapt Redshift distribution to a changing workload
  13. Read partitioned data after an additive schema change
  14. Promote catalog definitions between environments
  15. Organize analytics assets with searchable metadata
  16. Choose a vector index for document retrieval
  17. Combine vector and attribute search in OpenSearch
  18. Map source and target database data types
  19. Evolve Iceberg partitioning for new query patterns
3.1 Automate data processing18 questions
  1. Run dependent ETL steps in order
  2. Process many files in parallel
  3. Start processing when an S3 object arrives
  4. Manage a Python data workflow with a DAG
  5. Chain Glue jobs using completion conditions
  6. Run a recurring Redshift aggregation
  7. Wait for an external approval before continuing
  8. Connect an SQS queue to a processing workflow
  9. Retry transient ETL failures safely
  10. Backfill missing historical dates
  11. Process only new input with Glue
  12. Run intermittent Spark jobs without a permanent cluster
  13. Prevent duplicate aggregates after a retry
  14. Schedule a daily job in a business time zone
  15. Refresh a Parquet Athena table safely
  16. Notify operators when a workflow fails
  17. Limit shared-resource concurrency across DAGs
  18. Investigate DAG tasks stuck in the MWAA queue
3.2 Analyze data with AWS services18 questions
  1. Reduce Athena scan volume with partitions
  2. Return the latest order for each customer
  3. Align join-key data types in Athena
  4. Accelerate a repeated Redshift aggregation
  5. Query S3 data from Redshift
  6. Use a broadcast join for a small dimension
  7. Select the latest state per key in Spark
  8. Choose a refresh mode for a daily dashboard
  9. Gate publication on data-quality rules
  10. Prepare machine-learning data with a reusable flow
  11. Separate ETL and interactive Redshift workloads
  12. Estimate distinct users at lower cost
  13. Control Iceberg small files at scale
  14. Restrict dashboard rows by user
  15. Investigate a sudden Redshift join slowdown
  16. Reuse repeated Athena query results
  17. Switch analysis periods without duplicating dashboards
  18. Explore a data lake without managing a cluster
3.3 Monitor and maintain data pipelines17 questions
  1. Detect pipeline latency early
  2. Trace a distributed job failure
  3. Audit S3 object-level data access
  4. Alarm on a failed Glue job
  5. Monitor Kinesis stream processing lag
  6. Maintain a Redshift table after updates
  7. Detect an OpenSearch capacity problem
  8. Detect anomalies in a seasonal workload
  9. Detect tampering with audit logs
  10. Find a data-pipeline cost increase
  11. Resume a partially successful pipeline
  12. Separate search and long-term log retention
  13. Extract failed records from structured logs
  14. Alarm on a data-quality regression
  15. Create a single pipeline-health view
  16. Record pipeline versions for reproducibility
  17. Trace the cause of a delayed pipeline
3.4 Ensure data quality16 questions
  1. Detect missing required fields
  2. Detect duplicate events
  3. Validate numeric ranges
  4. Validate order-to-customer referential integrity
  5. Detect stale data
  6. Detect input schema drift
  7. Profile a large dataset with representative sampling
  8. Detect data skew
  9. Reconcile source and aggregate counts
  10. Quarantine outliers while continuing analysis
  11. Resolve duplicates across sources
  12. Detect a change in data distribution
  13. Design publication decisions for quality violations
  14. Retest corrected data
  15. Establish a producer-consumer quality contract
  16. Prevent duplicates from late-arriving events
4.1 Apply authentication13 questions
  1. Avoid long-lived keys in a Glue job
  2. Read a cross-account S3 bucket safely
  3. Retrieve database credentials securely
  4. Use a private path from a VPC to S3
  5. Privately publish a data service to another VPC
  6. Allow only ETL-to-database traffic
  7. Constrain a third-party role assumption
  8. Trace the principal of temporary credentials
  9. Reach Secrets Manager from an isolated subnet
  10. Restrict data access by network context
  11. Give an EC2 data process temporary access
  12. Separate authentication by SageMaker Unified Studio project
  13. Shift database operations to a managed service
4.2 Apply authorization11 questions
  1. Grant an analyst access to one Catalog table
  2. Share a table without exposing sensitive columns
  3. Manage data permissions with tags
  4. Separate S3 access by project tags
  5. Allow only one role to write a prefix
  6. Share a Catalog across accounts
  7. Limit roles created by developers
  8. Restrict rows by region
  9. Limit decryption to a customer's data
  10. Detect unintended external access
  11. Apply an organization-wide authorization guardrail
4.3 Apply encryption and masking12 questions
  1. Encrypt sensitive S3 data with a customer key
  2. Protect data in transit between processing services
  3. Encrypt with a KMS key owned by another account
  4. Manage KMS key rotation
  5. Choose a DynamoDB encryption key
  6. Mask personal data for analytics
  7. Tokenize payment identifiers
  8. Bind KMS decryption to a dataset
  9. Encrypt before uploading to S3
  10. Discover personal data in S3
  11. Separate KMS administration from data use
  12. Protect sensitive data for analytics
4.4 Prepare audit logs12 questions
  1. Centralize API auditing for all accounts
  2. Query audit events over the long term
  3. Detect abnormal API activity
  4. Stream service logs to a protected account
  5. Record requests made to an S3 bucket
  6. Audit Data Catalog permission changes
  7. Align retention with audit requirements
  8. Keep personal data out of logs
  9. Detect a surge in log activity
  10. Restrict who can read audit logs
  11. Submit reproducible evidence for an audit
  12. Centralize logs from large EMR jobs
4.5 Understand data privacy and governance11 questions
  1. Connect data classification to controls
  2. Define data owners and stewards
  3. Delete data after its retention period
  4. Remediate public exposure found by Macie
  5. Meet a data-residency requirement
  6. Trace PII into derived datasets
  7. Minimize data sent to analytics
  8. Map regulations to technical controls
  9. Review data access regularly
  10. Manage a data-governance exception
  11. Share datasets across teams without copying them