DEA-C01 Data Store Management Practice Question
A data engineer is designing a data store for a real-time analytics application that requires low-latency reads and writes at scale. The data model includes time-series data with high ingest rates and queries that aggregate data over sliding time windows. The engineer needs a fully managed AWS service that supports automatic scaling and can handle millions of writes per second. Which service should the engineer choose?
⚠ Common exam trap
The trap here is assuming that a relational database or data warehouse can handle the same scale and latency requirements as a purpose-built NoSQL service like DynamoDB.
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 database that provides low-latency performance at any scale, with automatic scaling and support for high-throughput workloads. It can handle millions of writes per second and is ideal for time-series data due to features like TTL and on-demand capacity. Its ability to scale horizontally without downtime makes it the best choice for real-time analytics applications requiring low-latency reads and writes.
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 S3
Why it's wrong here
Amazon S3 is an object storage service designed for durability and scalability, but it is not a database and does not support low-latency reads and writes for transactional workloads. While it can store time-series data, querying it with low latency requires additional services like Athena, which is not optimized for real-time analytics. S3 is not suitable for high-throughput write and read operations at millisecond latency.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a data warehouse optimized for analytical queries on large datasets, not for high-concurrency, low-latency transactional writes. It can handle high ingest rates through COPY or streaming ingestion, but it is not designed for millions of individual writes per second. Redshift is better for batch analytics and complex aggregations, not real-time operational workloads.
- ✗
Amazon RDS for PostgreSQL
Why it's wrong here
Amazon RDS for PostgreSQL is a relational database that can handle moderate throughput but does not automatically scale to millions of writes per second. It requires manual scaling (e.g., larger instance types) and may struggle with high-velocity ingest. While it supports time-series data with extensions like TimescaleDB, it is not fully managed for automatic scaling at the required scale, making it less suitable.
- ✓
Amazon DynamoDB
Why this is correct
DynamoDB is a fully managed NoSQL database that supports high-throughput reads and writes with automatic scaling. It can handle millions of requests per second and provides low-latency performance. With features like time-to-live (TTL) and on-demand capacity mode, it is well-suited for time-series data and real-time analytics. Its ability to scale horizontally without downtime makes it the best fit for this scenario.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.