DEA-C01 Data Store Management Practice Question
A company wants to store data from thousands of IoT devices with varying data rates. The data must be stored in a schema-on-read fashion and support SQL queries. Which AWS service should be used?
⚠ Common exam trap
Test-takers frequently confuse schema-on-read with schema-on-write, assuming DynamoDB's flexible schema or Redshift's SQL support fits, but they miss that DynamoDB lacks native SQL and Redshift requires upfront table definitions, while Athena directly queries raw files in S3 with SQL.
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
Amazon S3 stores data in its native format (e.g., JSON, Parquet) without requiring a predefined schema, enabling schema-on-read. Amazon Athena uses Presto-based SQL to query data directly from S3, making it ideal for IoT data with varying rates and ad-hoc SQL analysis without provisioning servers.
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 RDS for MySQL
Why it's wrong here
RDS requires a fixed schema defined before loading data.
- ✓
Amazon S3 with Amazon Athena
Why this is correct
S3 provides scalable storage, and Athena enables SQL queries with schema-on-read.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database requiring schema definition at write time.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a columnar data warehouse requiring schema definition.
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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