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. A key principle to apply: dAX provides an in-memory cache for DynamoDB tables.. 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 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.
Key principle: DAX provides an in-memory cache for DynamoDB tables.
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
DAX provides an in-memory cache for DynamoDB tables.
✗
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 confuse increasing throughput capacity (Option B) with reducing latency, not realizing that DynamoDB's storage latency is fixed and that caching (DAX) is the correct solution for repeated read-heavy workloads.
Detailed technical explanation
How to think about this question
DAX is a fully managed, in-memory cache that sits between your application and DynamoDB, using a write-through caching strategy to keep cached items consistent with the table. Under the hood, DAX maintains a cluster of nodes that store frequently accessed items in memory, and it supports both eventual and strong consistency reads, with a typical read latency of microseconds compared to DynamoDB's single-digit milliseconds. In a real-world scenario, a dashboard serving repeated queries for the same tenant data would see a dramatic reduction in latency because DAX serves cached results without hitting the table's provisioned throughput or incurring storage I/O.
KKey Concepts to Remember
DAX provides an in-memory cache for DynamoDB tables.
DAX is designed to accelerate eventually consistent reads.
DAX can reduce read latency from milliseconds to microseconds.
→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
DAX provides an in-memory cache for DynamoDB tables.
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.
Review dAX provides an in-memory cache for DynamoDB tables., then practise related SAA-C03 questions on the same topic to reinforce the concept.
Design High-Performing Architectures — This question tests Design High-Performing Architectures — DAX provides an in-memory cache for DynamoDB tables..
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 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.
What should I do if I get this SAA-C03 question wrong?
Review dAX provides an in-memory cache for DynamoDB tables., then practise related SAA-C03 questions on the same topic to reinforce the concept.
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?
DAX provides an in-memory cache for DynamoDB tables.
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