DEA-C01 Data Store Management Practice Question
A data engineer needs to store JSON documents that are accessed by a key-value pattern. The workload requires single-digit millisecond latency at any scale. Which AWS service is most appropriate?
⚠ Common exam trap
A common mix-up: candidates confuse DocumentDB's document storage capability with DynamoDB's key-value performance, overlooking that DocumentDB is not designed for single-digit millisecond latency at any scale, especially under high throughput.
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 DynamoDB
Amazon DynamoDB is a fully managed NoSQL key-value and document database that delivers single-digit millisecond latency at any scale. It is optimized for key-value access patterns, making it the ideal choice for storing and retrieving JSON documents by a primary key with consistent low latency.
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 DocumentDB (with MongoDB compatibility)
Why it's wrong here
DocumentDB stores JSON documents but does not provide single-digit millisecond latency at any scale; its MongoDB-compatible architecture scales differently. It is tempting because document storage matches the JSON requirement, yet the latency-at-any-scale demand points to a purpose-built key-value store instead.
- ✗
Amazon RDS for PostgreSQL
Why it's wrong here
RDS for PostgreSQL stores relational tables, not native key-value JSON access, and cannot deliver single-digit millisecond latency at any scale. It is tempting because PostgreSQL supports JSONB columns, but that suits relational workloads with moderate scale, not the key-value pattern demanded here.
- ✓
Amazon DynamoDB
Why this is correct
Amazon DynamoDB stores JSON as native items and retrieves them by primary key, delivering consistent single-digit millisecond latency regardless of table size. Its partition-based architecture scales horizontally without the query-engine overhead of Amazon Athena or the fixed schema of Amazon RDS, satisfying both the key-value access pattern and the any-scale latency constraint.
- ✗
Amazon Neptune
Why it's wrong here
Neptune is a graph database, so it traverses relationships rather than serving key-value document lookups, and its latency does not meet single-digit milliseconds at any scale. It is tempting because Neptune genuinely suits connected-data workloads such as knowledge graphs, fraud rings and recommendation engines where traversal queries dominate.
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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.