DEA-C01 Data Store Management Practice Question
A company is using an Amazon RDS for PostgreSQL database to store application data. The data engineering team needs to run complex analytical queries that join multiple large tables. These queries are causing performance degradation on the production database. The team wants to offload the analytical workload to a separate system that can handle large-scale data processing. Which AWS service should the team use?
⚠ Common exam trap
The trap here is assuming that any database can handle analytical queries, but transactional databases like RDS are not optimized for large-scale joins and aggregations.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Redshift
Amazon Redshift is purpose-built for analytical workloads, offering columnar storage, massively parallel processing, and advanced query optimization. By moving the analytical queries to Redshift, the team can achieve faster performance without impacting the production RDS database. This separation of transactional and analytical workloads is a common best practice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Redshift
Why this is correct
Amazon Redshift is a fully managed, petabyte-scale data warehouse designed for analytical queries. It uses columnar storage and massively parallel processing to handle complex joins and aggregations efficiently. Offloading analytical workloads to Redshift frees up the RDS production database and provides better performance for large-scale data processing.
- ✗
Amazon ElastiCache for Redis
Why it's wrong here
ElastiCache for Redis is an in-memory data store used for caching, session management, and real-time analytics. It does not support complex SQL joins or large-scale data processing. Using it for analytical queries would be inefficient and not address the need for a data warehouse.
- ✗
Amazon RDS for MySQL
Why it's wrong here
Amazon RDS for MySQL is another relational database, but it is not designed for large-scale analytical workloads. It would still face performance issues with complex joins on large tables. Migrating to another RDS engine does not provide the scalability and analytical capabilities of a data warehouse.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL key-value and document database designed for high-performance at scale, but it is not optimized for complex analytical queries involving joins. It lacks the SQL support and columnar storage needed for large-scale analytical processing. Therefore, it is not suitable for this workload.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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