The answer is to add a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it. This is the best way to reduce DynamoDB read latency because DAX acts as an in-memory cache that delivers microsecond response times for eventually consistent reads, eliminating the need to repeatedly query the underlying table for hot data. On the SAA-C03 exam, this scenario tests your understanding of caching layers versus table redesigns or application-level caching—a common trap is choosing to add more read capacity units, which only increases throughput, not speed. Remember that DAX is specifically designed for read-heavy workloads where low latency is critical, not for write optimization. Memory tip: DAX = Data Accelerator for eXtra speed, think “DAX slashes latency to the max.”
SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Exhibit
DynamoDB metrics and access pattern:
- Table mode: on-demand
- ConsumedReadCapacityUnits: steady, no throttling overall
- SuccessfulRequestLatency: p95 = 34 ms
- Hot partition key detected: tenant#42 consumes 92% of read traffic during peak
Application notes:
- Requests repeatedly fetch the same dashboard items for up to 60 seconds
- Reads are eventually consistent and the application can tolerate brief cache staleness
- Writes are infrequent and do not dominate the workload
Based on the exhibit, which design change is the best way to reduce the observed read latency for this DynamoDB-backed service?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
DynamoDB metrics and access pattern:
- Table mode: on-demand
- ConsumedReadCapacityUnits: steady, no throttling overall
- SuccessfulRequestLatency: p95 = 34 ms
- Hot partition key detected: tenant#42 consumes 92% of read traffic during peak
Application notes:
- Requests repeatedly fetch the same dashboard items for up to 60 seconds
- Reads are eventually consistent and the application can tolerate brief cache staleness
- Writes are infrequent and do not dominate the workload
A
Add a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it.
DAX is designed to accelerate repeated eventually consistent reads from DynamoDB by caching hot items in memory. The exhibit shows one tenant driving most of the reads and the same dashboard items being requested repeatedly within a short window, which is an excellent fit for DAX. It reduces latency and offloads the hot key without requiring a schema redesign.
B
Increase the on-demand table limits so DynamoDB can automatically absorb more traffic.
Why wrong: On-demand already scales capacity automatically, and the problem here is hot, repeated reads with cacheable data rather than missing capacity.
C
Create a global secondary index on tenantId to distribute the load across more partitions.
Why wrong: A GSI changes access paths, but it does not cache repeated reads and may add write overhead without solving the hot-read pattern.
D
Move the dashboard data into S3 and use Lambda functions to read it on demand.
Why wrong: That would replace a low-latency database with an object-storage workflow and would worsen the user-facing read path.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it.
Adding a DynamoDB Accelerator (DAX) cluster in front of the table reduces read latency by providing an in-memory cache for repeated read traffic. DAX delivers microsecond response times for eventually consistent reads, which directly addresses the observed latency issue without requiring application-level caching or table redesign.
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 a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it.
Why this is correct
DAX is designed to accelerate repeated eventually consistent reads from DynamoDB by caching hot items in memory. The exhibit shows one tenant driving most of the reads and the same dashboard items being requested repeatedly within a short window, which is an excellent fit for DAX. It reduces latency and offloads the hot key without requiring a schema redesign.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the on-demand table limits so DynamoDB can automatically absorb more traffic.
Why it's wrong here
On-demand already scales capacity automatically, and the problem here is hot, repeated reads with cacheable data rather than missing capacity.
✗
Create a global secondary index on tenantId to distribute the load across more partitions.
Why it's wrong here
A GSI changes access paths, but it does not cache repeated reads and may add write overhead without solving the hot-read pattern.
✗
Move the dashboard data into S3 and use Lambda functions to read it on demand.
Why it's wrong here
That would replace a low-latency database with an object-storage workflow and would worsen the user-facing read path.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing capacity limits (Option B) or adding indexes (Option C) will solve latency issues, but they fail to recognize that latency is a caching problem, not a throughput or partitioning problem, and that DAX is the AWS-native solution for DynamoDB read-heavy workloads with repeated access patterns.
Detailed technical explanation
How to think about this question
DAX acts as a write-through cache that intercepts read requests and serves them from memory if the item is present, avoiding the DynamoDB storage layer. For repeated reads of the same items, DAX can achieve single-digit millisecond latency, compared to DynamoDB's typical single-digit to tens of milliseconds for eventually consistent reads. In a multi-tenant dashboard scenario where users frequently refresh the same data, DAX significantly reduces the load on the table's partitions and improves overall responsiveness.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it. — Adding a DynamoDB Accelerator (DAX) cluster in front of the table reduces read latency by providing an in-memory cache for repeated read traffic. DAX delivers microsecond response times for eventually consistent reads, which directly addresses the observed latency issue without requiring application-level caching or table redesign.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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. Based on the exhibit, which design change is the best way to reduce the observed read latency for this DynamoDB-backed service?
hard
✓ A.Add a DynamoDB Accelerator (DAX) cluster in front of the table and send repeated read traffic through it.
B.Increase the on-demand table limits so DynamoDB can automatically absorb more traffic.
C.Create a global secondary index on tenantId to distribute the load across more partitions.
D.Move the dashboard data into S3 and use Lambda functions to read it on demand.
Why A: Adding a DynamoDB Accelerator (DAX) cluster in front of the table reduces read latency by providing an in-memory cache that serves repeated read requests with microsecond response times, bypassing the need to read from the underlying DynamoDB table's SSD storage. This directly addresses the observed latency issue for frequently accessed data, as DAX is optimized for read-heavy workloads and supports eventual and strong consistency reads.
Last reviewed: Jun 11, 2026
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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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