- A
Use DynamoDB Accelerator (DAX) for the table.
DAX provides an in-memory cache, significantly reducing read latency for repeated queries that retrieve all attributes. This directly addresses the latency issue for high-volume sensors.
- B
Create a global secondary index (GSI) with the same key structure but projecting only the required attributes.
Why wrong: A GSI with projection only includes a subset of attributes. Since the application retrieves all attributes, queries would need to fetch missing attributes from the base table, adding latency and defeating the purpose.
- C
Enable DynamoDB Streams to replicate data to Amazon ElastiCache.
Why wrong: DynamoDB Streams to ElastiCache is overly complex and introduces eventual consistency and additional management overhead. DAX is a simpler and more direct caching solution.
- D
Increase the read capacity units (RCUs) on the table.
Why wrong: The table uses on-demand capacity, which automatically scales. Increasing RCUs is unnecessary and does not address the root cause of large data reads per query.
Reducing DynamoDB Read Latency with GSI Projection
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: dynamoDB Accelerator (DAX). 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 uses Amazon DynamoDB to store IoT sensor data. Each sensor sends data every minute. The table has a partition key of sensor_id and a sort key of timestamp. The application queries data for a sensor over the last hour. The table uses on-demand capacity. Recently, the query latency increased for sensors that generate a high volume of data. The application retrieves all attributes for the sensor data. Which design change should be made to reduce latency?
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 Accelerator (DAX) for the table.
The latency increase is due to high data volume per sensor, causing large reads from the base table. DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency by serving frequently accessed data directly from memory, avoiding expensive DynamoDB read operations. Option B (GSI with projection) would not help because the application retrieves all attributes, so any missing attributes in the GSI would require additional fetches from the base table, increasing latency. Option C adds complexity and Option D is irrelevant since on-demand capacity already scales.
Key principle: DynamoDB Accelerator (DAX)
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 Accelerator (DAX) for the table.
Why this is correct
DAX provides an in-memory cache, significantly reducing read latency for repeated queries that retrieve all attributes. This directly addresses the latency issue for high-volume sensors.
Related concept
DynamoDB Accelerator (DAX)
- ✗
Create a global secondary index (GSI) with the same key structure but projecting only the required attributes.
Why it's wrong here
A GSI with projection only includes a subset of attributes. Since the application retrieves all attributes, queries would need to fetch missing attributes from the base table, adding latency and defeating the purpose.
- ✗
Enable DynamoDB Streams to replicate data to Amazon ElastiCache.
Why it's wrong here
DynamoDB Streams to ElastiCache is overly complex and introduces eventual consistency and additional management overhead. DAX is a simpler and more direct caching solution.
- ✗
Increase the read capacity units (RCUs) on the table.
Why it's wrong here
The table uses on-demand capacity, which automatically scales. Increasing RCUs is unnecessary and does not address the root cause of large data reads per query.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume that projecting only required attributes into a GSI always reduces read size, but when the application retrieves all attributes, the GSI may not have all columns, requiring extra base table fetches and increasing latency. The real solution is to use DAX to cache the full items.
Detailed technical explanation
How to think about this question
When a DynamoDB query retrieves all attributes, it reads the entire item from the base table, including any large or unused attributes. A GSI with projected attributes allows the query to read only the needed columns from the index, which is typically smaller and faster. Under the hood, DynamoDB charges for read capacity based on the size of the items read, so projecting fewer attributes reduces both latency and cost. In real-world scenarios, IoT sensors often store metadata or raw payloads that are not needed for time-series queries, making GSI projection a key optimization.
KKey Concepts to Remember
- DynamoDB Accelerator (DAX)
- GSI Projection
- On-demand Capacity
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
DynamoDB Accelerator (DAX)
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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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 — DynamoDB Accelerator (DAX).
What is the correct answer to this question?
The correct answer is: Use DynamoDB Accelerator (DAX) for the table. — The latency increase is due to high data volume per sensor, causing large reads from the base table. DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency by serving frequently accessed data directly from memory, avoiding expensive DynamoDB read operations. Option B (GSI with projection) would not help because the application retrieves all attributes, so any missing attributes in the GSI would require additional fetches from the base table, increasing latency. Option C adds complexity and Option D is irrelevant since on-demand capacity already scales.
What should I do if I get this DBS-C01 question wrong?
Review dynamoDB Accelerator (DAX), then practise related DBS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
DynamoDB Accelerator (DAX)
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Last reviewed: Jun 24, 2026
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