DEA-C01 Data Store Management Practice Question
A data engineer needs to store time-series data from IoT devices. The data is write-heavy and requires low-latency queries by device ID and timestamp. The data volume is expected to grow to terabytes. Which AWS database service is most suitable?
⚠ Common exam trap
A common mix-up: candidates choose DynamoDB (Option C) because of its high write throughput and low-latency queries, but they overlook the lack of native time-series optimizations, leading to complex manual partitioning and TTL management that Timestream handles automatically.
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 Timestream
Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) to handle write-heavy IoT workloads at scale. It supports low-latency queries by device ID and timestamp via its SQL-compatible query engine, making it the most suitable choice for terabytes of time-series data.
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 for MySQL is a relational engine whose single-writer primary and B-tree indexes handle sustained IoT write rates and terabyte time-series range scans poorly. It is tempting because it offers familiar SQL and managed backups, making it the right choice for transactional, moderate-volume workloads rather than high-ingest telemetry.
- ✗
Amazon ElastiCache for Redis
Why it's wrong here
ElastiCache for Redis is an in-memory cache, not a durable primary datastore; terabytes of IoT writes would exceed practical memory limits and risk data loss on eviction or restart. It is tempting because it delivers microsecond latency for device-ID lookups, making it the right choice as a caching layer in front of a persistent time-series store.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value store, not purpose-built for time-series; range queries by timestamp alongside device ID require composite sort-key design and lack native time-window functions. It is tempting because it handles write-heavy, terabyte-scale workloads with single-digit-millisecond latency, suiting high-volume key lookups rather than time-series analytics.
- ✓
Amazon Timestream
Why this is correct
Amazon Timestream is purpose-built for time-series workloads, using a memory store for recent data and a magnetic store for historical data. This satisfies the stem's write-heavy, low-latency-by-device-and-timestamp, terabyte-scale requirements, which general-purpose relational or key-value services cannot match as efficiently.
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
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.