AWS Certified Data Engineer - Associate DEA-C01
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Free AWS Certified Data Engineer - Associate DEA-C01 Questions
A company needs to implement data lake access analytics to understand which data is most valuable. Which AWS service provides data access analytics?
- A. AWS CloudTrail with access analysis
- B. Amazon Macie
- C. S3 Access Analyzer and CloudTrail Insights
- D. AWS Config
Explanation
S3 Access Analyzer combined with CloudTrail data events provides insights into data access patterns, including which objects are accessed, by whom, and how frequently. This helps understand data value and optimize storage. Macie finds sensitive data, Config tracks configuration, and standard CloudTrail logs access but doesn't provide analytics.
Correct Answer Is:
C
A data pipeline must process data with ACID transaction guarantees in a data lake. Which AWS service provides ACID transactions on S3?
- A. AWS Lake Formation governed tables
- B. S3 versioning
- C. AWS Glue with transactional writes
- D. Redshift Spectrum
Explanation
AWS Lake Formation governed tables (built on Apache Iceberg) provide ACID transaction guarantees on S3 data including concurrent read/write support, rollback, and time travel. S3 versioning doesn't provide ACID semantics, standard Glue doesn't guarantee ACID, and Spectrum is query-only.
Correct Answer Is:
A
A data pipeline loads data from multiple time zones. The engineer needs to standardize timestamps to UTC. Where should this transformation occur?
- A. At data source before ingestion
- B. During ETL in AWS Glue or Lambda
- C. In the data warehouse after loading
- D. Never standardize, store as-is
Explanation
Transforming timestamps to UTC during ETL (in Glue or Lambda) before loading into the data warehouse ensures consistent time representation for analytics. Source transformation may not be possible, post-load transformation is inefficient and may miss records, and storing without standardization causes query complications.
Correct Answer Is:
B
A data engineer is designing a data lake architecture. Which AWS service provides a centralized metadata catalog for data stored across multiple sources?
- A. AWS Glue Data Catalog
- B. Amazon DynamoDB
- C. AWS Systems Manager Parameter Store
- D. Amazon DocumentDB
Explanation
AWS Glue Data Catalog is a centralized metadata repository that stores structural and operational metadata for all data assets. It integrates with services like Athena, Redshift Spectrum, and EMR, providing a unified view of data across multiple sources. DynamoDB is a NoSQL database, Parameter Store stores configuration data, and DocumentDB is a document database.
Correct Answer Is:
A
A data pipeline processes clickstream data using AWS Glue. The engineer notices data skew causing some workers to take much longer. What is the BEST solution?
- A. Increase Glue DPU allocation
- B. Repartition data using repartition() or coalesce() before processing
- C. Use smaller input files
- D. Increase worker memory
Explanation
Repartitioning data using repartition() or coalesce() redistributes data evenly across workers, addressing skew. This ensures balanced processing. Increasing DPUs adds resources but doesn't fix skew, smaller files may not address the root cause, and increasing worker memory doesn't redistribute data.
Correct Answer Is:
B
A data pipeline must ensure that data loaded into Redshift matches the source data exactly. Which validation approach should be used?
- A. Row count comparison only
- B. AWS DMS data validation feature
- C. Hash/checksum comparison of data samples
- D. All of the above for comprehensive validation
Explanation
Comprehensive validation includes row counts, hash/checksum validation on data samples, and AWS DMS's built-in data validation feature (if using DMS). Row count alone misses data corruption, while hash comparisons detect content differences. Multi-layered validation ensures data integrity. DMS validation automates comparison for migrations.
Correct Answer Is:
D
A company needs to perform data discovery and classification on sensitive data in S3. Which AWS service automatically identifies PII and sensitive data?
- A. AWS Glue DataBrew
- B. Amazon Macie
- C. AWS Config
- D. Amazon Inspector
Explanation
Amazon Macie uses machine learning to automatically discover, classify, and protect sensitive data including PII in S3. It provides alerts and dashboards for sensitive data exposure. Glue DataBrew profiles data but doesn't specialize in PII detection, Config tracks resource compliance, and Inspector assesses security vulnerabilities.
Correct Answer Is:
B
A data pipeline processes large XML files from S3. The engineer needs to transform XML to JSON efficiently. Which service is MOST appropriate?
- A. AWS Lambda with XML parsing libraries
- B. AWS Glue with custom XML parsing in PySpark
- C. EMR with Spark XML library
- D. Athena with XML SerDe
Explanation
AWS Glue with custom XML parsing using Python libraries (like xmltodict) in PySpark handles large XML files efficiently with auto-scaling. Lambda has payload and timeout limits for large files, EMR requires cluster management, and Athena XML SerDe has limitations for complex XML structures.
Correct Answer Is:
B
A data pipeline processes data using AWS Glue. The engineer wants to optimize costs by using the minimum required DPU. Which approach identifies the optimal DPU count?
- A. Always use maximum DPUs for faster processing
- B. Start with minimum DPUs, monitor CloudWatch metrics, and adjust based on job duration
- C. Use default DPU count without monitoring
- D. Randomly test different DPU counts
Explanation
Start with minimum DPUs (2 for standard jobs), monitor CloudWatch metrics like job duration and resource utilization, then increase DPUs if jobs take too long or show resource constraints. This balances cost and performance. Maximum DPUs waste money, defaults may not be optimal, and random testing is inefficient.
Correct Answer Is:
B
A data engineer needs to migrate a 10 PB data warehouse from on-premises to AWS. Network bandwidth is 1 Gbps. Which migration strategy is MOST efficient?
- A. AWS DataSync over Direct Connect
- B. Multiple AWS Snowball Edge devices in parallel
- C. AWS DMS continuous replication
- D. S3 Transfer Acceleration
Explanation
Multiple AWS Snowball Edge devices (100 TB each) used in parallel provide the most efficient migration for 10 PB. Even with 1 Gbps (125 MB/s), transferring 10 PB takes ~925 days. Snowball devices can be used in parallel and shipped to AWS. DataSync would be slow, DMS is for databases, and Transfer Acceleration still uses limited network bandwidth.
Correct Answer Is:
B
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