DEA-C01 Data Store Management Practice Question
A data engineer needs to store semi-structured JSON log files from multiple sources and query them using SQL. The data is rarely updated and access frequency is low. Which storage solution is MOST cost-effective?
⚠ Common exam trap
Many exam-takers choose Redshift or RDS because they associate SQL querying with traditional databases, overlooking that Athena's serverless, pay-per-query model is far more cost-effective for infrequent access to static data stored 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 S3 with Amazon Athena for querying.
Amazon S3 with Athena is the most cost-effective solution because the data is semi-structured JSON, rarely updated, and accessed infrequently. S3 provides low-cost storage for static data, and Athena uses a serverless, pay-per-query model, eliminating the need for a running cluster or provisioned capacity. This combination avoids the fixed costs of Redshift, DynamoDB, or RDS, making it ideal for low-frequency SQL querying of archival logs.
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 Redshift with JSON ingestion and compression.
Why it's wrong here
Redshift provisions always-on compute clusters billed continuously, so storing rarely accessed logs there incurs cost regardless of query volume. It suits frequent analytical queries over large structured datasets, whereas the low-access, semi-structured requirement points to a serverless query service over object storage.
- ✗
Amazon DynamoDB with JSON documents.
Why it's wrong here
DynamoDB bills for provisioned or on-demand read and write capacity plus storage, and its key-based access patterns do not support ad hoc SQL over JSON logs. It suits high-throughput transactional item lookups, not infrequent analytical SQL queries across semi-structured log files.
- ✓
Amazon S3 with Amazon Athena for querying.
Why this is correct
Amazon S3 provides low-cost durable storage for semi-structured JSON, and Athena queries it in place using SQL without loading or provisioning servers. For rarely updated, infrequently accessed logs, this serverless combination avoids the ongoing cost of a data warehouse or database.
- ✗
Amazon RDS for PostgreSQL with JSONB columns.
Why it's wrong here
RDS for PostgreSQL runs a continuously billed instance with provisioned storage, and JSONB suits transactional records rather than bulk log files. It is tempting because JSONB supports SQL querying, but the low-access, rarely updated log workload fits querying directly over object storage instead.
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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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to store semi-structured JSON logs from multiple microservices in a cost-effective manner for later analysis using Amazon Athena. The logs are generated continuously, and the total volume is about 1 TB per day. The data must be queryable within minutes of arrival. Which storage solution is most appropriate?
easy- A.Amazon DynamoDB table with JSON attribute
- B.Amazon RDS for PostgreSQL table with JSON column
- ✓ C.Amazon S3 bucket with partitioned folders
- D.Amazon Redshift cluster with JSON ingestion
Why C: Amazon S3 with partitioned folders is the most appropriate solution because it provides a cost-effective, scalable storage layer for semi-structured JSON logs, and integrates natively with Amazon Athena for serverless querying. By partitioning the data by time (e.g., year/month/day/hour), Athena can use partition pruning to minimize scanned data, enabling queries within minutes of arrival. S3's low cost per GB and lifecycle policies further optimize storage for the 1 TB/day volume.
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.