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Workload-Specific Database DesignhardMultiple ChoiceObjective-mapped

DBS-C01 Workload-Specific Database Design Practice Question

A company runs a time-series application that collects sensor data from millions of IoT devices. The data is written in batches every minute and queried to generate hourly, daily, and monthly aggregates. The database must support high ingestion rates and efficient storage. Which database service is most appropriate?

⚠ Common exam trap

Many exam-takers choose Amazon DynamoDB for high ingestion rates, overlooking that time-series workloads require efficient time-based aggregation and storage optimization, which DynamoDB lacks, while Timestream is the only AWS service purpose-built for this exact use case.

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 a serverless architecture that automatically scales to handle high ingestion rates from millions of IoT devices. It optimizes storage by separating recent data (in memory) from historical data (in a magnetic store), and its built-in aggregation functions (e.g., `BIN`, `DATE_BIN`) efficiently compute hourly, daily, and monthly aggregates without manual partitioning or indexing.

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 DynamoDB with TTL

    Why it's wrong here

    DynamoDB can store time-series but requires careful design and does not have built-in aggregation functions.

  • Amazon Redshift

    Why it's wrong here

    Redshift is for analytical queries on large datasets, not real-time ingestion.

  • Amazon RDS for PostgreSQL

    Why it's wrong here

    RDS is not optimized for high-ingestion time-series workloads.

  • Amazon Timestream

    Why this is correct

    Timestream is purpose-built for time-series data, with automatic storage tiering and aggregate functions.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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Same concept, more angles

1 more way 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 company is designing a database for an IoT application that collects sensor data every second from millions of devices. The data is time-series and must be stored for 90 days, with occasional queries for recent data. Which AWS database solution is MOST cost-effective and performant?

medium
  • A.Amazon Timestream
  • B.Amazon Redshift
  • C.Amazon DynamoDB with TTL to expire old data
  • D.Amazon RDS for MySQL with partitioning

Why A: Amazon Timestream is purpose-built for time-series data, offering automatic storage tiering (in-memory for recent data and magnetic for historical data) and serverless scaling, which makes it the most cost-effective and performant choice for ingesting sensor data every second from millions of devices and retaining it for 90 days with occasional queries on recent data.

Last reviewed: Jun 24, 2026

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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.