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Workload-Specific Database Design →mediumMultiple SelectObjective-mapped

Quick Answer

The correct answers are Amazon Timestream with device_id as dimension and timestamp as time column, and DynamoDB with device_id as partition key and timestamp as sort key. Timestream is purpose-built for IoT time-series database design, automatically optimizing storage and query performance for sensor data ingested at high frequency, while its schema of dimensions and time columns directly maps to the device_id and timestamp fields in this workload. DynamoDB’s composite key design enables efficient range queries using a KeyConditionExpression on the sort key, and its partition-based scaling handles the high write throughput from thousands of devices, with multi-AZ replication ensuring high durability. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your ability to distinguish between a managed time-series service and a NoSQL key-value store for time-series patterns—a common trap is choosing a relational database like RDS, which lacks the write scaling and time-range query efficiency required here. Memory tip: for IoT time-series, think “Timestream for turnkey, DynamoDB for DIY partitioning.”

DBS-C01 Workload-Specific Database Design Practice Question

This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A company is designing a database for an IoT application that ingests sensor data from thousands of devices. Each device sends a reading every minute. The data includes device_id, timestamp, temperature, humidity, and pressure. The application needs to store this data and support queries that retrieve all readings for a specific device within a time range. The company expects high write throughput and moderate read frequency. The data must be stored with high durability. Which TWO database designs are appropriate for this workload? (Choose TWO.)

Question 1mediummulti select

Full question →

A
Use Amazon DynamoDB with device_id as partition key and store all readings for a device as a list attribute in a single item, updating the list every minute.
Why wrong: The list would grow too large and exceed the 400 KB item limit.
B
Use Amazon S3 to store compressed JSON files per device per hour, and query using Amazon Athena.
Why wrong: This is not suitable for real-time queries and high write frequency.
C
Use Amazon DynamoDB with device_id as partition key and timestamp as sort key.
DynamoDB can handle high write throughput and efficient queries by device and time range.
D
Use Amazon RDS for MySQL with a single table and index on device_id and timestamp.
Why wrong: A single RDS instance cannot scale to high write throughput from thousands of devices.
E
Use Amazon Timestream, a time series database, with device_id as dimension and timestamp as time column.
Timestream is purpose-built for time series data and handles high write throughput.

Full breakdown with real-world context →

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

✓

Use Amazon DynamoDB with device_id as partition key and timestamp as sort key.

Option C is correct because DynamoDB's partition key (device_id) and sort key (timestamp) design allows efficient retrieval of all readings for a specific device within a time range using a Query operation with a KeyConditionExpression on the sort key. This schema supports high write throughput by distributing writes across partitions based on device_id, and DynamoDB's multi-AZ replication provides high durability.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use Amazon DynamoDB with device_id as partition key and store all readings for a device as a list attribute in a single item, updating the list every minute.
Why it's wrong here
The list would grow too large and exceed the 400 KB item limit.
✗
Use Amazon S3 to store compressed JSON files per device per hour, and query using Amazon Athena.
Why it's wrong here
This is not suitable for real-time queries and high write frequency.
✓
Use Amazon DynamoDB with device_id as partition key and timestamp as sort key.
Why this is correct
DynamoDB can handle high write throughput and efficient queries by device and time range.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon RDS for MySQL with a single table and index on device_id and timestamp.
Why it's wrong here
A single RDS instance cannot scale to high write throughput from thousands of devices.
✓
Use Amazon Timestream, a time series database, with device_id as dimension and timestamp as time column.
Why this is correct
Timestream is purpose-built for time series data and handles high write throughput.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often overlook DynamoDB's item size limit and write hotspot issues in Option A, or assume that any SQL database can handle high write throughput without considering single-writer bottlenecks in Option D.

Detailed technical explanation

How to think about this question

DynamoDB's adaptive capacity automatically adjusts partition splits based on access patterns, but a single partition key like device_id can still cause hot partitions if a few devices generate disproportionate traffic; using a composite key with a sort key enables efficient range queries via the Query API, which retrieves items in sort key order without scanning. Timestream, as a purpose-built time series database, automatically manages data retention, compression, and time-based partitioning, making it ideal for IoT sensor data with high write throughput and range queries on timestamp.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this DBS-C01 question test?

Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use Amazon DynamoDB with device_id as partition key and timestamp as sort key. — Option C is correct because DynamoDB's partition key (device_id) and sort key (timestamp) design allows efficient retrieval of all readings for a specific device within a time range using a Query operation with a KeyConditionExpression on the sort key. This schema supports high write throughput by distributing writes across partitions based on device_id, and DynamoDB's multi-AZ replication provides high durability.

What should I do if I get this DBS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Same concept, more angles

1 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 company is selecting a database for a time-series application that collects sensor data from thousands of devices. The data is written at a high velocity (millions of data points per second). The application needs to query recent data (last hour) with sub-second latency and perform long-term analysis on months of data. Which TWO AWS database services best meet these requirements?

easy

A.Amazon ElastiCache for Redis with Time Series module.
✓ B.Amazon Timestream for both real-time and historical queries.
✓ C.Amazon DynamoDB with TTL and export to S3 for historical analysis.
D.Amazon Redshift for real-time queries and historical analysis.
E.Amazon Quantum Ledger Database (QLDB) for immutable time-series records.

Why B: Option A (Timestream) is built for time-series data with fast queries. Option D (DynamoDB with TTL) can handle high write throughput and automatically expire old data to S3 for analysis. Option B is wrong because Redshift is not designed for sub-second real-time queries. Option C is wrong because ElastiCache is primarily a cache. Option E is wrong because QLDB is for ledger data.

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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.