- A
Use the same partition key (customerId), but increase the table’s provisioned capacity for that tenant.
Why wrong: Increasing provisioned capacity can raise the overall throughput ceiling, but DynamoDB throttling for a hot partition is driven by per-partition limits (and uneven item/key distribution), not just by the table’s total capacity. If nearly all ACME writes still target the same partition (same customerId value), that single partition can remain the bottleneck, so throttling and p99 latency can persist during spikes.
- B
Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key.
Hot-partition throttling happens when a single logical partition (one partition key value) receives more requests than it can serve. By salting the partition key (for example, customerId#shardId), ACME’s writes are spread across multiple physical partitions, reducing request rate per partition and lowering throttling. Efficient reads for ACME can be preserved by querying only the shard partitions that belong to ACME (for example, using a small, deterministic set of shardIds and issuing parallel queries per shard, then merging results). This avoids scanning the whole table and keeps access patterns predictable while improving tail latency.
- C
Switch to on-demand capacity mode and keep the partition key unchanged.
Why wrong: On-demand capacity automatically scales the table’s overall capacity, but it does not remove per-partition request limits. If all ACME traffic still targets one partition key value, that partition can still become hot and throttle, so p99 latency and ProvisionedThroughputExceeded events can continue during spikes.
- D
Enable Global Tables so that reads are served from a nearby replica for ACME.
Why wrong: Global Tables can reduce network latency and improve availability across regions, but they do not address hot partitions within a region. If ACME’s writes concentrate on a single partition key value, the corresponding partition in each replica can still be the throttling bottleneck for that tenant.
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.
Your mobile app writes events to a single DynamoDB table with partition key = customerId and sort key = eventTime. During a promotional campaign, one tenant ("ACME") generates far more traffic than others. CloudWatch shows sustained throttling (ProvisionedThroughputExceeded) and elevated p99 latency only for that tenant. The workload pattern cannot be changed to a completely different schema, but you can change how items are partitioned. Which design change is most likely to reduce the hot-partition throttling while keeping efficient reads for ACME?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key.
Option B is correct because salting the partition key by appending a shard number (e.g., customerId + random digit) distributes ACME's writes across multiple partitions, eliminating the hot partition. The sort key still preserves eventTime ordering, so queries for a specific customer can be parallelized across shards and merged client-side or via a composite sort key pattern, maintaining efficient reads.
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.
- ✗
Use the same partition key (customerId), but increase the table’s provisioned capacity for that tenant.
Why it's wrong here
Increasing provisioned capacity can raise the overall throughput ceiling, but DynamoDB throttling for a hot partition is driven by per-partition limits (and uneven item/key distribution), not just by the table’s total capacity. If nearly all ACME writes still target the same partition (same customerId value), that single partition can remain the bottleneck, so throttling and p99 latency can persist during spikes.
- ✓
Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key.
Why this is correct
Hot-partition throttling happens when a single logical partition (one partition key value) receives more requests than it can serve. By salting the partition key (for example, customerId#shardId), ACME’s writes are spread across multiple physical partitions, reducing request rate per partition and lowering throttling. Efficient reads for ACME can be preserved by querying only the shard partitions that belong to ACME (for example, using a small, deterministic set of shardIds and issuing parallel queries per shard, then merging results). This avoids scanning the whole table and keeps access patterns predictable while improving tail latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to on-demand capacity mode and keep the partition key unchanged.
Why it's wrong here
On-demand capacity automatically scales the table’s overall capacity, but it does not remove per-partition request limits. If all ACME traffic still targets one partition key value, that partition can still become hot and throttle, so p99 latency and ProvisionedThroughputExceeded events can continue during spikes.
- ✗
Enable Global Tables so that reads are served from a nearby replica for ACME.
Why it's wrong here
Global Tables can reduce network latency and improve availability across regions, but they do not address hot partitions within a region. If ACME’s writes concentrate on a single partition key value, the corresponding partition in each replica can still be the throttling bottleneck for that tenant.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing capacity or switching to on-demand alone solves hot partitions, but they overlook DynamoDB's fixed per-partition throughput limits that require key design changes to distribute load.
Detailed technical explanation
How to think about this question
Under the hood, DynamoDB partitions data by partition key hash; a single partition can handle up to 3,000 RCU or 1,000 WCU. Salting with a shard number (e.g., customerId_0 through customerId_N) spreads writes across N partitions, each with its own throughput limit. For reads, you can query all shards in parallel using a scatter-gather pattern or use a global secondary index with a different key structure to avoid merging overhead.
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
Got this wrong? Here's your next step.
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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: Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key. — Option B is correct because salting the partition key by appending a shard number (e.g., customerId + random digit) distributes ACME's writes across multiple partitions, eliminating the hot partition. The sort key still preserves eventTime ordering, so queries for a specific customer can be parallelized across shards and merged client-side or via a composite sort key pattern, maintaining efficient reads.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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