- A
Shard the hot tenant’s writes across multiple partition key values so traffic is spread across partitions.
Write sharding reduces pressure on a single partition and is the standard fix for a hot key caused by one tenant.
- B
Use Amazon DAX to cache repetitive read requests for the same items with sub-millisecond latency.
DAX accelerates repeated reads and reduces pressure on DynamoDB when dashboards request the same recent data repeatedly.
- C
Switch the table to on-demand capacity mode so DynamoDB automatically removes partition limits.
Why wrong: On-demand mode simplifies capacity management, but it does not remove the physical partition throughput limits of a hot key.
- D
Use a larger sort key attribute to increase the maximum write throughput for the tenant.
Why wrong: Sort key size does not change partition-level throughput, so it does not solve the hot partition bottleneck.
- E
Move the table to a single larger provisioned throughput setting and keep the same key design.
Why wrong: Raising table capacity without changing the access pattern still leaves one partition overloaded by the same tenant key.
Quick Answer
The correct answer is to implement DynamoDB hot key throttling mitigation with sharding and DAX, specifically by sharding the hot tenant’s partition key and caching reads with Amazon DAX. Sharding works by appending a random or timestamp-based suffix to the partition key value, which spreads writes across multiple physical partitions and avoids hitting the per-partition throughput limit of 1,000 WCU or 3,000 RCU. DAX then caches repetitive read requests for the same items, delivering sub-millisecond latency and offloading read traffic from the table. On the SAA-C03 exam, this scenario tests your understanding of DynamoDB’s partition mechanics and the need to separate write scaling from read caching—a common trap is to only add DAX without fixing the write bottleneck, or to over-provision RCU instead of sharding. Remember the mnemonic: “Shard the hot key, cache the hot read.”
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 multi-tenant event system writes and reads data in DynamoDB. One tenant generates most of the traffic, causing throttling on a single partition key value, and the dashboards repeatedly read the most recent items for that tenant. Which two changes should the team make to improve performance? 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
Shard the hot tenant’s writes across multiple partition key values so traffic is spread across partitions.
Option A is correct because sharding the hot tenant's writes across multiple partition key values (e.g., by appending a random suffix or a timestamp-based suffix) distributes the write traffic across multiple physical partitions, avoiding throttling on a single partition. This is a common DynamoDB design pattern for mitigating hot keys, as each partition has its own throughput limits (e.g., up to 3,000 RCU or 1,000 WCU for a partition). By spreading writes, the system can achieve higher aggregate throughput without hitting per-partition limits.
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.
- ✓
Shard the hot tenant’s writes across multiple partition key values so traffic is spread across partitions.
Why this is correct
Write sharding reduces pressure on a single partition and is the standard fix for a hot key caused by one tenant.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Amazon DAX to cache repetitive read requests for the same items with sub-millisecond latency.
Why this is correct
DAX accelerates repeated reads and reduces pressure on DynamoDB when dashboards request the same recent data repeatedly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch the table to on-demand capacity mode so DynamoDB automatically removes partition limits.
Why it's wrong here
On-demand mode simplifies capacity management, but it does not remove the physical partition throughput limits of a hot key.
- ✗
Use a larger sort key attribute to increase the maximum write throughput for the tenant.
Why it's wrong here
Sort key size does not change partition-level throughput, so it does not solve the hot partition bottleneck.
- ✗
Move the table to a single larger provisioned throughput setting and keep the same key design.
Why it's wrong here
Raising table capacity without changing the access pattern still leaves one partition overloaded by the same tenant key.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume on-demand mode (Option C) or higher provisioned throughput (Option E) will solve hot key throttling, but they fail to recognize that DynamoDB's per-partition throughput limits are independent of the table's capacity mode or total provisioned capacity.
Detailed technical explanation
How to think about this question
Under the hood, DynamoDB partitions data based on the partition key's hash value; each partition has a fixed throughput capacity (1,000 WCU and 3,000 RCU). When a single partition key (e.g., tenant ID) receives excessive traffic, it can exceed that partition's limits, causing throttling. Sharding the hot key by appending a random suffix (e.g., tenantID-1, tenantID-2) distributes writes across multiple partitions, but reads must then query all shards and merge results—this is a trade-off. For the dashboard's repeated reads of the most recent items, using DAX (Option B) caches the query results, reducing read load on DynamoDB and providing sub-millisecond latency, which complements the write sharding strategy.
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: Shard the hot tenant’s writes across multiple partition key values so traffic is spread across partitions. — Option A is correct because sharding the hot tenant's writes across multiple partition key values (e.g., by appending a random suffix or a timestamp-based suffix) distributes the write traffic across multiple physical partitions, avoiding throttling on a single partition. This is a common DynamoDB design pattern for mitigating hot keys, as each partition has its own throughput limits (e.g., up to 3,000 RCU or 1,000 WCU for a partition). By spreading writes, the system can achieve higher aggregate throughput without hitting per-partition limits.
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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