DEA-C01 Data Store Management Practice Question
A company uses Amazon S3 to store large datasets for analytics. Each dataset is stored in a separate prefix and consists of thousands of small objects (1-10 KB each). The company notices that listing objects in a prefix takes several seconds, slowing down data processing. Which solution would MOST improve listing performance?
⚠ Common exam trap
Test-takers frequently confuse S3 Select (which filters object content) with filtering object keys during listing, or assume that parallel requests to a single prefix are allowed, when in fact S3 throttles ListObject calls per prefix and parallelism only helps across different prefixes.
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 S3 Inventory to generate a daily listing of objects.
S3 Inventory provides a scheduled CSV/Parquet file listing all objects in a bucket or prefix, including metadata like size and last modified date. By querying this inventory file instead of issuing real-time ListObject API calls, you avoid the latency of enumerating thousands of small objects, dramatically improving listing performance for analytics workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a lifecycle policy to transition objects to S3 Glacier.
Why it's wrong here
Lifecycle policies do not affect listing performance.
- ✗
Use S3 Select to filter objects during listing.
Why it's wrong here
S3 Select is for content retrieval, not for listing objects.
- ✓
Use S3 Inventory to generate a daily listing of objects.
Why this is correct
S3 Inventory provides a pre-generated list that can be queried quickly.
- ✗
Increase the number of parallel requests by using more prefixes.
Why it's wrong here
More prefixes improve throughput but not the latency of a single listing operation.
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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