DEA-C01 Data Store Management Practice Question
A company runs an Amazon Redshift cluster with 10 RA3 nodes. The data warehouse stores 50 TB of data. The company notices that queries are slow and the cluster's storage utilization is high. The data engineer needs to improve query performance and reduce storage costs without changing the cluster's node count. Which action should the engineer take?
⚠ Common exam trap
Many candidates confuse concurrency scaling (which improves query throughput) with storage optimization, or they assume that changing distribution styles (like DISTSTYLE ALL) will always improve performance, ignoring the storage cost impact in a high-utilization scenario.
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 Redshift Spectrum to offload historical data to Amazon S3 and query it in place.
Redshift Spectrum allows you to query data directly from Amazon S3 without loading it into the cluster. By offloading historical or less-frequently accessed data to S3, you reduce the storage utilization on the RA3 nodes, which frees up managed storage and can improve query performance. This approach also lowers storage costs because S3 is cheaper than Redshift managed storage, and it does not change the node count.
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 Redshift Spectrum to offload historical data to Amazon S3 and query it in place.
Why this is correct
Spectrum queries data in S3, reducing cluster storage and allowing faster queries on hot data.
- ✗
Change the distribution style of large tables to DISTSTYLE ALL.
Why it's wrong here
DISTSTYLE ALL increases storage consumption by replicating data.
- ✗
Migrate the cluster to Dense Compute node types.
Why it's wrong here
RA3 nodes already have managed storage; Dense Compute nodes are not an upgrade.
- ✗
Enable concurrency scaling to handle more concurrent queries.
Why it's wrong here
Concurrency scaling adds compute but does not reduce storage utilization.
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
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