DBS-C01 Workload-Specific Database Design Practice Question
A startup is building a social media analytics platform. The workload is write-heavy, with millions of events per day containing user actions (likes, shares, comments). The data model is simple: each event is a JSON document with a timestamp, user ID, and action type. Queries are primarily aggregations over time (e.g., count of likes per hour) and require low-latency responses for dashboards. The team wants to minimize operational overhead and cost. Which database service is most appropriate?
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, a purpose-built time-series database.
Amazon Timestream is the most appropriate service because it is purpose-built for time-series data, supporting high write throughput and providing built-in aggregation functions for time-based queries. It is serverless, minimizing operational overhead and cost. Option A (ElastiCache) is a caching layer, not a durable database. Option B (RDS with TimescaleDB) requires manual scaling and management, increasing overhead. Option D (DynamoDB) is optimized for key-value access, not efficient for time-series aggregations without additional processing and secondary indexes.
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 ElastiCache for Redis to store aggregated counts.
Why it's wrong here
ElastiCache is not a durable data store.
- ✗
Amazon RDS for PostgreSQL with TimescaleDB extension.
Why it's wrong here
Possible but adds operational overhead; not as serverless as Timestream.
- ✓
Amazon Timestream, a purpose-built time-series database.
Why this is correct
Timestream is designed for high write throughput and time-based aggregations.
- ✗
Amazon DynamoDB with global secondary indexes on timestamp and action type.
Why it's wrong here
DynamoDB is not optimized for time-series aggregations; would require scan operations.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Same concept, more angles
2 more ways this is tested on DBS-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 startup is building a social media analytics platform that requires storing time-series data with frequent writes and queries for the last hour. Which AWS database service is BEST suited for this workload?
easy- ✓ A.Amazon Timestream
- B.Amazon RDS with MySQL
- C.Amazon Neptune
- D.Amazon DynamoDB
Why A: Amazon Timestream is a purpose-built time-series database that efficiently handles high-frequency writes and recent-time queries (e.g., last hour) with automatic data tiering (in-memory store for recent data, magnetic store for historical). Its serverless architecture and built-in time-series functions (e.g., interpolation, smoothing) make it ideal for social media analytics workloads requiring low-latency queries on recent data.
Variation 2. A company wants to store and analyze time-series sensor data from millions of IoT devices. The data is append-only and rarely updated. Queries aggregate data over time ranges. Which AWS database service is most cost-effective and performant for this workload?
easy- A.Amazon DynamoDB with time-series design pattern
- ✓ B.Amazon Timestream
- C.Amazon Redshift
- D.Amazon RDS for MySQL with partitioning by date
Why B: Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical data) and optimized query performance for time-range aggregations. Its serverless architecture eliminates provisioning overhead, making it the most cost-effective and performant choice for append-only IoT sensor data with infrequent updates.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DBS-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 DBS-C01 exam.