- A
Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX.
Amazon DAX is an in-memory caching layer for DynamoDB that accelerates repeated reads. When many clients request the same items (for example, “latest status” point reads by deviceId), DAX can serve cached responses directly, reducing round trips to DynamoDB and lowering read latency during peak periods.
- B
Change the partition key to a random value for each request to eliminate hot partitions.
Why wrong: The scenario states partition distribution is already healthy, so randomizing the partition key does not target the actual problem (repeat reads of the same items). It also breaks the access pattern because the application can no longer reliably request items by deviceId, and it can reduce usability and query correctness.
- C
Increase write capacity only, because writes generally determine read latency in DynamoDB.
Why wrong: Write capacity does not directly address read latency for repeatedly accessed items. Read latency is primarily influenced by read capacity, item size, network latency, and whether reads are being accelerated via caching (such as DAX). Increasing writes may increase overall workload contention without fixing the repeated-read issue.
- D
Create an additional Global Secondary Index (GSI) and read exclusively from the index to accelerate reads.
Why wrong: A GSI can support alternate query patterns or access paths, but it does not provide caching for repeated point reads. Creating a GSI changes how items are accessed and billed; it is not as direct as using DAX to reduce latency for repeated reads of the same keys.
SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. 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 DynamoDB table stores device status items. The partition key is deviceId, and the partition distribution is healthy (no single partition dominates). However, during peak periods the application experiences high read latency because many clients repeatedly request the latest status for the same devices. Which action best improves read latency without changing the DynamoDB partitioning model?
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
Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX.
Amazon DAX is a fully managed, in-memory cache for DynamoDB that provides microsecond read latency. By caching the results of repeated GetItem and Query requests for the same device status items, DAX offloads read traffic from the underlying DynamoDB table, reducing the number of read capacity units consumed and eliminating the latency caused by repeated fetches from disk. This directly addresses the high read latency during peak periods without altering the existing partition key or partitioning model.
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.
- ✓
Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX.
Why this is correct
Amazon DAX is an in-memory caching layer for DynamoDB that accelerates repeated reads. When many clients request the same items (for example, “latest status” point reads by deviceId), DAX can serve cached responses directly, reducing round trips to DynamoDB and lowering read latency during peak periods.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the partition key to a random value for each request to eliminate hot partitions.
Why it's wrong here
The scenario states partition distribution is already healthy, so randomizing the partition key does not target the actual problem (repeat reads of the same items). It also breaks the access pattern because the application can no longer reliably request items by deviceId, and it can reduce usability and query correctness.
- ✗
Increase write capacity only, because writes generally determine read latency in DynamoDB.
Why it's wrong here
Write capacity does not directly address read latency for repeatedly accessed items. Read latency is primarily influenced by read capacity, item size, network latency, and whether reads are being accelerated via caching (such as DAX). Increasing writes may increase overall workload contention without fixing the repeated-read issue.
When this WOULD be correct
This option would be correct if the question described high write latency or write throttling during peak periods, and the solution required increasing write capacity to handle the write load without changing the partitioning model.
- ✗
Create an additional Global Secondary Index (GSI) and read exclusively from the index to accelerate reads.
Why it's wrong here
A GSI can support alternate query patterns or access paths, but it does not provide caching for repeated point reads. Creating a GSI changes how items are accessed and billed; it is not as direct as using DAX to reduce latency for repeated reads of the same keys.
When this WOULD be correct
A DynamoDB table has a suboptimal partition key leading to hot partitions, and you need to improve read performance by distributing reads across partitions. Creating a GSI with a different partition key can spread the read load and reduce latency.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The SAA-C03 exam frequently reuses these exact scenarios with slightly different constraints.
✓Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX.Correct answer▾
Why this is correct
Amazon DAX is an in-memory caching layer for DynamoDB that accelerates repeated reads. When many clients request the same items (for example, “latest status” point reads by deviceId), DAX can serve cached responses directly, reducing round trips to DynamoDB and lowering read latency during peak periods.
✗Increase write capacity only, because writes generally determine read latency in DynamoDB.Wrong answer — click to see why▾
Why this is wrong here
Increasing write capacity does not reduce read latency; read latency is affected by read capacity and throttling, not write capacity. The problem is high read demand on the same items, which write capacity cannot address.
★ When this WOULD be the correct answer
This option would be correct if the question described high write latency or write throttling during peak periods, and the solution required increasing write capacity to handle the write load without changing the partitioning model.
Why candidates choose this
Candidates may mistakenly think that writes and reads are coupled in DynamoDB, or that increasing any capacity will improve overall performance, not realizing that read and write capacities are independent.
✗Create an additional Global Secondary Index (GSI) and read exclusively from the index to accelerate reads.Wrong answer — click to see why▾
Why this is wrong here
Creating a GSI does not reduce read latency for repeated requests to the same items; it only provides an alternate query pattern. The hot partition issue is caused by high read frequency on specific items, which a GSI does not alleviate.
★ When this WOULD be the correct answer
A DynamoDB table has a suboptimal partition key leading to hot partitions, and you need to improve read performance by distributing reads across partitions. Creating a GSI with a different partition key can spread the read load and reduce latency.
Why candidates choose this
Candidates may think that indexes always speed up reads, not realizing that GSIs don't cache data or reduce the load on the base table's partitions for repeated identical queries.
Analysis generated from the official SAA-C03blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think a GSI can magically speed up reads, but GSIs do not provide caching and still read from the same storage layer, so they do not reduce latency for repeated identical queries.
Trap categories for this question
Scenario analysis trap
The scenario states partition distribution is already healthy, so randomizing the partition key does not target the actual problem (repeat reads of the same items). It also breaks the access pattern because the application can no longer reliably request items by deviceId, and it can reduce usability and query correctness.
Detailed technical explanation
How to think about this question
DAX acts as a write-through cache, meaning that when data is written to DynamoDB, it is also written to the cache, ensuring consistency for subsequent reads. Under the hood, DAX uses an in-memory engine that stores frequently accessed items, and it automatically evicts least-recently-used data when memory is full. In a real-world scenario, a fleet management system where thousands of clients poll the latest status of the same 100 devices every second would see a dramatic reduction in read latency from single-digit milliseconds to microseconds by using DAX, while also reducing DynamoDB read costs.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this SAA-C03 question test?
Design High-Performing Architectures — This question tests Design High-Performing Architectures — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX. — Amazon DAX is a fully managed, in-memory cache for DynamoDB that provides microsecond read latency. By caching the results of repeated GetItem and Query requests for the same device status items, DAX offloads read traffic from the underlying DynamoDB table, reducing the number of read capacity units consumed and eliminating the latency caused by repeated fetches from disk. This directly addresses the high read latency during peak periods without altering the existing partition key or partitioning model.
What should I do if I get this SAA-C03 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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Last reviewed: Jun 11, 2026
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