2.0 Data Store Management DEA-C01 Practice Quiz
130 exam-style questions covering 26% of the DEA-C01 exam. Instant feedback on every answer, progress tracking, no signup required.
This domain is part of the AWS Certified Data Engineer Associate practice test. Each question is tagged by exam objective and difficulty so you can drill exactly the areas you need.
Sample Questions
A data warehouse must support complex analytical SQL over terabytes of curated relational data with predictable query performance. Which service should be selected?
- A. Amazon Redshift
- B. Amazon MemoryDB for Redis
- C. Amazon Aurora PostgreSQL pgvector
- D. Amazon CloudWatch
Amazon Redshift is designed for analytical SQL workloads over large structured datasets and provides warehouse features for performance-oriented reporting. This is the correct answer.
A relational application workload needs transactions, SQL, and managed database operations. Which service is suitable?
- A. Amazon RDS
- B. Amazon DynamoDB Streams
- C. AWS Transfer Family
- D. AWS CloudTrail
Amazon RDS provides managed relational database engines for transactional SQL workloads with backups, patching, and availability features. This is the correct answer.
A low-latency application needs single-digit millisecond key-value lookups at high scale. Which data store is most appropriate?
- A. Amazon S3 Tables
- B. Amazon DynamoDB
- C. AWS DMS Schema Conversion
- D. Amazon S3
DynamoDB provides managed key-value and document storage with low-latency access patterns and automatic scaling options for high-throughput applications. This is the correct answer.
A DynamoDB table is queried by customerId and orderDate. What configuration should align with this access pattern?
- A. Amazon DynamoDB Streams
- B. Partition and sort keys that support customerId and orderDate queries
- C. Apache ORC
- D. Amazon S3 Tables
A partition key and sort key designed around customerId and orderDate allow DynamoDB to retrieve the requested items efficiently without table scans. This is the correct answer.
A DynamoDB workload needs an alternate query pattern that the base table key does not support. Which configuration helps?
- A. Amazon Kinesis Data Firehose
- B. A global secondary index
- C. Amazon S3
- D. AWS CloudFormation
A global secondary index provides another key structure so the application can query DynamoDB efficiently by a different attribute pattern. This is the correct answer.
A data lake stores raw and curated files in open formats and needs low-cost durable storage. Which storage service should be used?
- A. Amazon OpenSearch Service
- B. AWS CloudFormation
- C. Amazon S3
- D. AWS DMS Schema Conversion
Amazon S3 provides durable, low-cost object storage that is commonly used as the foundation for raw and curated data lake zones. This is the correct answer.
An Amazon Redshift table is frequently joined on customer_id and filtered by event_date. What should be reviewed?
- A. AWS Snow Family
- B. Amazon SageMaker Data Wrangler
- C. Distribution style, sort keys, and table design
- D. Amazon Kinesis Data Streams
Redshift distribution and sort choices affect join movement and scan pruning, so they should match frequent join and filter patterns. This is the correct answer.
A Spark analytics workload requires configurable distributed processing over data in S3. Which service should be considered?
- A. Amazon Keyspaces
- B. Amazon SageMaker Unified Studio
- C. AWS Glue crawlers
- D. Amazon EMR
Amazon EMR runs distributed frameworks such as Spark and can process large datasets with configurable cluster resources and storage integration. This is the correct answer.
Key Terms in This Domain
- AWS Systems Manager Parameter Store: Hierarchical secure storage for configuration data and secrets
- AWS Glue crawlers: Automatically infer schemas and populate the Data Catalog
- Amazon S3 Glacier: Long-term archive storage with low cost
- Amazon S3 Lifecycle policies: Automate object transitions and expiration for cost control
- AWS IAM: Identity and access management for AWS resources
- Apache Hive metastore: Schema/table metadata service for Hadoop ecosystems
- Compression: Reduce storage and I/O cost using formats like Snappy, Gzip, Zstd
- AWS Glue: Serverless ETL service for data preparation and integration
- AWS Glue Data Catalog: Central technical metadata catalog for data lakes
- AWS Glue DataBrew: Visual data preparation tool with no-code transformations
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Other DEA-C01 Domains
- 1.0 Data Ingestion and Transformation
- 3.0 Data Operations and Support
- 4.0 Data Security and Governance
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