- A
Put Amazon DynamoDB Accelerator (DAX) in front of the table.
DAX is an in-memory cache for DynamoDB reads, so repeated lookups for the same keys can be served with much lower latency than direct table reads. It is especially effective for hot-item access patterns like order lookups, product metadata, and profile reads.
- B
Use eventually consistent reads where the application can tolerate slightly stale data.
Eventually consistent reads avoid the extra requirement of reading the most recent committed value on every request. When slight staleness is acceptable, they fit well with caching layers and help keep read-path latency low.
- C
Switch all access to strongly consistent reads for faster results.
Why wrong: Strongly consistent reads prioritize reading the latest data, but that freshness guarantee does not make reads faster. In fact, the stronger consistency requirement can reduce cache usefulness and can increase latency compared with a cache-friendly read path.
- D
Increase the item size so fewer requests are needed.
Why wrong: Larger items usually cost more to transfer and can take longer to read and deserialize. Increasing item size does not improve lookup latency and often makes the performance problem worse.
- E
Replace the table with Amazon EBS volumes mounted on EC2 instances.
Why wrong: EBS is block storage attached to an EC2 instance, not a managed key-value database. Replacing DynamoDB with EBS would change the storage model completely and would not preserve the low-latency lookup behavior the application needs.
Quick Answer
The answer is to use eventually consistent reads and implement DynamoDB Accelerator (DAX). This combination directly addresses the need for low latency on repeated reads of the same few items because DAX acts as an in-memory cache, delivering microsecond response times by serving hot data from memory rather than from disk, while eventually consistent reads avoid the overhead of waiting for the latest write confirmation. On the SAA-C03 exam, this scenario tests your understanding of when to trade strict consistency for performance; a common trap is choosing strongly consistent reads or a separate caching layer like ElastiCache, which would require more application changes. The key insight is that DAX is purpose-built for DynamoDB and requires minimal code changes—just swapping the client SDK. Remember the mnemonic “DAD” for this pattern: DynamoDB + DAX + Eventually Consistent = lowest latency with minimal effort.
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.
An order lookup API repeatedly reads the same few items from DynamoDB. The application can tolerate slightly stale data for a few seconds, and the team wants the lowest-latency design with minimal application changes. Which two changes should they make? Select two.
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
Put Amazon DynamoDB Accelerator (DAX) in front of the table.
Amazon DynamoDB Accelerator (DAX) is an in-memory cache for DynamoDB that provides microsecond read latency, which is ideal for repeated reads of the same few items. Since the application can tolerate slightly stale data, DAX's default write-through caching with a TTL of 5 minutes ensures low latency without requiring application code changes beyond adding the DAX client.
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.
- ✓
Put Amazon DynamoDB Accelerator (DAX) in front of the table.
Why this is correct
DAX is an in-memory cache for DynamoDB reads, so repeated lookups for the same keys can be served with much lower latency than direct table reads. It is especially effective for hot-item access patterns like order lookups, product metadata, and profile reads.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use eventually consistent reads where the application can tolerate slightly stale data.
Why this is correct
Eventually consistent reads avoid the extra requirement of reading the most recent committed value on every request. When slight staleness is acceptable, they fit well with caching layers and help keep read-path latency low.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch all access to strongly consistent reads for faster results.
Why it's wrong here
Strongly consistent reads prioritize reading the latest data, but that freshness guarantee does not make reads faster. In fact, the stronger consistency requirement can reduce cache usefulness and can increase latency compared with a cache-friendly read path.
- ✗
Increase the item size so fewer requests are needed.
Why it's wrong here
Larger items usually cost more to transfer and can take longer to read and deserialize. Increasing item size does not improve lookup latency and often makes the performance problem worse.
- ✗
Replace the table with Amazon EBS volumes mounted on EC2 instances.
Why it's wrong here
EBS is block storage attached to an EC2 instance, not a managed key-value database. Replacing DynamoDB with EBS would change the storage model completely and would not preserve the low-latency lookup behavior the application needs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think strongly consistent reads are always faster, but they actually have higher latency and cannot be cached by DAX, making them unsuitable for this low-latency, minimal-change requirement.
Detailed technical explanation
How to think about this question
DAX acts as a write-through cache that intercepts GetItem and Query calls, serving cached items from memory with sub-millisecond latency. For eventually consistent reads, DynamoDB returns data from a replica within one second of a write, which is sufficient for the stated tolerance of a few seconds. The combination of DAX caching and eventually consistent reads minimizes both latency and cost, as strongly consistent reads consume twice the read capacity units.
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 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 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: Put Amazon DynamoDB Accelerator (DAX) in front of the table. — Amazon DynamoDB Accelerator (DAX) is an in-memory cache for DynamoDB that provides microsecond read latency, which is ideal for repeated reads of the same few items. Since the application can tolerate slightly stale data, DAX's default write-through caching with a TTL of 5 minutes ensures low latency without requiring application code changes beyond adding the DAX client.
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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on SAA-C03
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 service performs many repeated read requests for the same DynamoDB items. The reads are latency-sensitive, but the application can tolerate slightly stale data. Which AWS service is the best fit to reduce read latency?
easy- ✓ A.Amazon DAX (DynamoDB Accelerator)
- B.Amazon S3 Select
- C.Amazon SQS FIFO queue
- D.AWS Lambda provisioned concurrency
Why A: Amazon DAX (DynamoDB Accelerator) is an in-memory cache specifically designed for DynamoDB. It reduces read latency from single-digit milliseconds to microseconds by caching frequently accessed items, and it supports eventually consistent reads, which aligns with the application's tolerance for slightly stale data. DAX handles repeated read requests without additional DynamoDB read capacity unit consumption, making it the optimal choice for this latency-sensitive workload.
Last reviewed: Jun 11, 2026
This SAA-C03 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 SAA-C03 exam.
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