DEA-C01 Data Store Management Practice Question
A data engineer needs to store semi-structured JSON transaction logs for analytics. The logs are written once and rarely accessed. The storage must be cost-effective. Which AWS service should be used?
⚠ Common exam trap
It's easy for candidates to choose DynamoDB or Redshift because they support JSON natively, but they overlook the core requirement of cost-effective storage for rarely accessed data, which is best met by S3's low-cost object storage and lifecycle management features.
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 S3
Amazon S3 is the correct choice because it provides highly durable, cost-effective object storage ideal for semi-structured JSON transaction logs that are written once and rarely accessed. S3's lifecycle policies can automatically transition such infrequently accessed data to S3 Glacier or S3 Glacier Deep Archive for even lower storage costs, making it the most economical option for this use case.
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 S3
Why this is correct
Amazon S3 provides durable object storage with tiered classes such as S3 Standard-IA and Glacier, matching the write-once, rarely accessed, cost-sensitive requirement. It natively holds semi-structured JSON, and analytics tools query it directly without provisioning servers.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value store priced for high-throughput, low-latency item access, with provisioned or on-demand capacity charges; write-once, rarely read JSON logs pay for query capability they never use. It would be correct for serving millisecond lookups on known partition keys, not cost-effective archival analytics.
- ✗
Amazon RDS
Why it's wrong here
Amazon RDS is a relational engine for structured, transactional workloads with provisioned compute, so it cannot ingest JSON logs cost-effectively at scale. RDS suits query-heavy OLTP applications needing joins and ACID transactions, not write-once, rarely read semi-structured archives.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a provisioned MPP data warehouse for structured, frequently queried analytical workloads, not cheap write-once JSON retention. Its RA3 nodes bill continuously whether or not the logs are read. It would suit aggregating transformed transaction data into dimensional models for BI reporting, not archiving raw semi-structured logs.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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