DEA-C01 Data Store Management Practice Question
A data engineer needs to store semi-structured JSON logs from multiple microservices in a cost-effective manner for ad-hoc querying using SQL. Which AWS service should be used?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Athena with Amazon Kinesis Data Analytics, mistakenly thinking that Kinesis is the go-to service for SQL-based log analysis, when in fact Kinesis is for real-time streaming and Athena is the correct serverless query service for stored data in S3.
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 in S3
Amazon Athena is the correct choice because it allows you to query semi-structured JSON logs stored in S3 directly using standard SQL, without needing to load or transform the data. Athena's schema-on-read approach and pay-per-query pricing make it highly cost-effective for ad-hoc analysis of large volumes of log data, as you only pay for the data scanned during queries.
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 Athena with data in S3
Why this is correct
Amazon Athena queries JSON directly from S3 using schema-on-read, so no transformation or loading is needed before ad-hoc SQL analysis. S3 provides the cheapest durable storage for semi-structured logs, satisfying the cost-effectiveness constraint, while Athena's pay-per-query model avoids provisioning clusters for intermittent querying.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB stores items by primary key, not JSON documents queried with SQL; its PartiQL support is limited and not designed for ad-hoc analytical SQL over log files. It is tempting because it is a cost-effective serverless store, and would suit high-throughput key-value lookups rather than log analytics.
- ✗
Amazon RDS for MySQL
Why it's wrong here
Amazon RDS for MySQL stores data in relational tables with a fixed schema, so semi-structured JSON requires shredding or JSON column workarounds and offers no serverless pay-per-query model. It suits transactional relational workloads, whereas Amazon Athena queries JSON in S3 directly.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics processes streaming data with SQL in motion, not stored semi-structured logs queried ad-hoc later. It is tempting because it uses SQL and handles JSON streams, and would be correct for real-time transformation or continuous analytics on live microservice telemetry.
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
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,321-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.