DEA-C01 Data Store Management Practice Question
A data engineer needs to store log files from multiple applications in a centralized location. The logs are generated in JSON format and each log entry is about 1 KB. The engineer needs to query the logs occasionally using SQL-like queries. Which AWS service is most appropriate?
⚠ Common exam trap
A common mix-up: candidates choose Amazon Redshift or RDS because they think 'SQL-like queries' require a traditional database, overlooking Athena's ability to query data directly in S3 without loading it, which is a key serverless pattern for log analytics.
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 Athena with data stored in S3
Amazon Athena is the most appropriate service because it allows you to query log files stored in S3 directly using standard SQL, without needing to load or transform the data. Since the logs are in JSON format and each entry is about 1 KB, Athena's schema-on-read approach works perfectly for occasional SQL-like queries, and you only pay for the data scanned per query, making it cost-effective for infrequent access.
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 DynamoDB
Why it's wrong here
Amazon DynamoDB is a key-value store queried by primary key or index, not SQL; it cannot run SQL-like queries over JSON log entries without additional tooling. It would be correct for low-latency item lookups at scale, but not for centralised log storage with occasional SQL-style analysis.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a provisioned columnar data warehouse built for large-scale analytical aggregation across structured tables, so loading 1 KB JSON log entries individually and querying them occasionally wastes cluster capacity and needs schema transformation. It fits periodic heavy BI reporting, not ad hoc log queries.
- ✓
Amazon Athena with data stored in S3
Why this is correct
Athena queries JSON in S3 directly using standard SQL, charging only per query scanned, which suits occasional log analysis. S3 provides the centralised, durable store for the 1 KB JSON entries, so no database loading or cluster is required.
- ✗
Amazon RDS for MySQL
Why it's wrong here
Amazon RDS for MySQL stores rows in a provisioned relational schema, requiring the JSON logs to be transformed and loaded before querying, and it scales for transactional workloads rather than cheap centralised log storage. It would suit an application needing ACID transactions, not occasional SQL queries over raw JSON files.
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
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.