SAA-C03 Design High-Performing Architectures Practice Question

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?

⚠ Common exam trap

Test-takers frequently 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.

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 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.

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.
✗
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.

Go deeper

SAA-C03 Design High-Performing Architectures Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question