DEA-C01 Data Store Management Practice Question
A company has an Amazon Redshift cluster with a mix of frequently accessed hot data and rarely accessed cold data. They want to reduce storage costs without affecting query performance for the hot data. Which strategy is MOST effective?
⚠ Common exam trap
Watch out — candidates often choose Redshift Spectrum (Option C) thinking it automatically offloads cold data, but Spectrum requires manual external table creation and does not integrate with the cluster's automatic storage tiering.
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
✓
Use RA3 nodes with managed storage to automatically offload cold data to Amazon S3.
RA3 nodes with managed storage automatically separate compute and storage, offloading cold data to Amazon S3 while keeping hot data on local SSD for fast queries. This reduces storage costs without manual intervention or affecting hot data performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use RA3 nodes with managed storage to automatically offload cold data to Amazon S3.
Why this is correct
RA3 nodes separate compute from Redshift Managed Storage, automatically moving cold blocks to Amazon S3 while hot data stays on local SSD. This lowers storage cost without degrading hot-data query performance, and needs no manual data movement.
- ✗
Reduce the number of nodes and increase the number of slices.
Why it's wrong here
Fewer nodes with more slices reduces total cluster compute and storage capacity, degrading hot-data query performance instead of preserving it. It is tempting because slices parallelise scans within a node, and would be the right lever when optimising distribution and parallelism on a fixed-size cluster.
- ✗
Create external tables in Redshift Spectrum to query cold data in S3.
Why it's wrong here
Redshift Spectrum external tables still require the cluster to parse and scan S3 data at query time, so cold-data queries consume cluster compute and can affect hot-data performance. It is tempting because Spectrum genuinely lowers storage cost by leaving cold data in S3, and would suit a workload where cold queries are infrequent and latency-tolerant.
- ✗
Use Dense Compute nodes and unload cold data to Amazon S3 manually.
Why it's wrong here
Unloading cold data to S3 manually removes it from Redshift, so any query needing that data fails until it is reloaded, and Dense Compute nodes raise per-node cost rather than lower it. It is tempting because manual S3 tiering does cut cluster storage, and would fit archival data never queried again.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.