Question 862 of 1,730
Workload-Specific Database DesignmediumMultiple ChoiceObjective-mapped

Using Local Secondary Index for Time Range Queries in DynamoDB

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. A key principle to apply: time to Live (TTL). 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.

An IoT company ingests sensor data into Amazon DynamoDB. The data has a partition key of device_id and sort key of timestamp. Queries often filter by device_id and a date range. Which design pattern improves query performance and reduces cost?

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 DynamoDB Time to Live (TTL) to expire old data

Option A is correct because DynamoDB Time to Live (TTL) automatically expires and deletes old sensor data. By removing obsolete items, the table size is reduced, leading to faster queries (less data to scan) and lower storage costs. Additionally, fewer read capacity units are consumed because queries process fewer items. This pattern both improves query performance and reduces cost for time-series data where queries focus on recent date ranges.

Key principle: Time to Live (TTL)

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 DynamoDB Time to Live (TTL) to expire old data

    Why this is correct

    Correct. TTL reduces data volume, improving query performance and reducing storage and read costs.

    Related concept

    Time to Live (TTL)

  • Create a local secondary index on device_id and timestamp

    Why it's wrong here

    Incorrect. The base table already has device_id as partition key and timestamp as sort key, enabling efficient range queries. Adding an LSI on the same attributes is redundant and does not improve performance or reduce cost.

  • Enable DynamoDB Accelerator (DAX)

    Why it's wrong here

    Incorrect. DAX is an in-memory cache that improves read performance but adds cost for the DAX cluster. It does not reduce overall cost.

  • Create a global secondary index on timestamp

    Why it's wrong here

    Incorrect. A GSI on timestamp allows queries by timestamp only, not by device_id. A query for a specific device_id would still require a full scan of the index, which is inefficient.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap is that candidates overlook TTL as a performance tool. They often assume an index is needed when the base table already supports the query pattern. TTL not only saves storage cost but also speeds up queries by reducing the data set.

Detailed technical explanation

How to think about this question

A local secondary index (LSI) maintains the same partition key as the base table (device_id) but allows a different sort key (timestamp), enabling efficient range queries (e.g., BETWEEN, >, <) on timestamp per device. LSIs are stored in the same partition as the base table data, ensuring strong consistency and low latency, but they consume additional write capacity for index updates. In practice, this pattern is ideal for time-series IoT data where you need to retrieve all readings for a specific device within a given date range without scanning unrelated partitions.

KKey Concepts to Remember

  • Time to Live (TTL)
  • Base table partition and sort key

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

Time to Live (TTL)

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Review time to Live (TTL), then practise related DBS-C01 questions on the same topic to reinforce the concept.

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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 — Time to Live (TTL).

What is the correct answer to this question?

The correct answer is: Use DynamoDB Time to Live (TTL) to expire old data — Option A is correct because DynamoDB Time to Live (TTL) automatically expires and deletes old sensor data. By removing obsolete items, the table size is reduced, leading to faster queries (less data to scan) and lower storage costs. Additionally, fewer read capacity units are consumed because queries process fewer items. This pattern both improves query performance and reduces cost for time-series data where queries focus on recent date ranges.

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

Review time to Live (TTL), then practise related DBS-C01 questions on the same topic to reinforce the concept.

What is the key concept behind this question?

Time to Live (TTL)

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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.