- A
Increase the table’s provisioned read capacity units while keeping partition key = customerId.
Why wrong: Increasing provisioned read capacity can raise the overall throughput, but it does not address the root cause of hot partitions: all reads for a given customerId target the same partition(s). If a single partition must serve most reads, throttling can still occur even with more total capacity.
- B
Add a salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp.
Hot partition throttling usually occurs when too many requests target a single partition key value. Salting transforms the partition key so that one high-traffic customerId maps to multiple distinct partition keys (e.g., customerId#0, customerId#1, etc.), which increases the number of partitions that can serve that customer’s workload concurrently and reduces the probability that a single partition becomes overloaded.
- C
Remove the sort key and use timestamp as the partition key to increase cardinality.
Why wrong: Using timestamp as the partition key may increase the number of distinct partition key values, but it does not guarantee distribution aligned with the customer’s read hotspot. During a campaign, traffic volume per customer is still the dominant factor; reads could still concentrate on the same time windows or key values relative to query patterns.
- D
Switch to on-demand capacity and rely on DynamoDB to automatically distribute reads across partitions.
Why wrong: On-demand capacity can absorb bursts by provisioning capacity dynamically, but it does not change how requests map to partitions. If the partition key design causes a single partition key value to receive a disproportionate share of traffic, throttling behavior can still appear for that hot partition even in on-demand mode.
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 DynamoDB table uses this schema: partition key = customerId, sort key = timestamp. During a marketing campaign, one customer generates extremely high read traffic and the application sees ProvisionedThroughputExceeded errors even though the table’s total capacity is sufficient. What change most directly improves read distribution across partitions?
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 salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp.
Option B is correct because adding a salt to the partition key (e.g., customerId#hash(requestId)) distributes the read-heavy customer's data across multiple physical partitions. This prevents a single hot partition from throttling requests, even when the table's total provisioned capacity is sufficient. DynamoDB's partition key determines the internal hash used for data placement, so increasing partition key cardinality directly improves read distribution.
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.
- ✗
Increase the table’s provisioned read capacity units while keeping partition key = customerId.
Why it's wrong here
Increasing provisioned read capacity can raise the overall throughput, but it does not address the root cause of hot partitions: all reads for a given customerId target the same partition(s). If a single partition must serve most reads, throttling can still occur even with more total capacity.
When this WOULD be correct
This option would be correct if the question described a scenario where overall table throughput is insufficient due to uniformly high traffic across all partitions, and the goal is simply to increase total capacity without a hot key issue.
- ✓
Add a salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp.
Why this is correct
Hot partition throttling usually occurs when too many requests target a single partition key value. Salting transforms the partition key so that one high-traffic customerId maps to multiple distinct partition keys (e.g., customerId#0, customerId#1, etc.), which increases the number of partitions that can serve that customer’s workload concurrently and reduces the probability that a single partition becomes overloaded.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove the sort key and use timestamp as the partition key to increase cardinality.
Why it's wrong here
Using timestamp as the partition key may increase the number of distinct partition key values, but it does not guarantee distribution aligned with the customer’s read hotspot. During a campaign, traffic volume per customer is still the dominant factor; reads could still concentrate on the same time windows or key values relative to query patterns.
When this WOULD be correct
If the question described a scenario where the access pattern is time-series with uniform read traffic across all timestamps (e.g., reading all records from the last hour for analytics), and the issue was low cardinality of the original partition key, then using timestamp as partition key could improve distribution.
- ✗
Switch to on-demand capacity and rely on DynamoDB to automatically distribute reads across partitions.
Why it's wrong here
On-demand capacity can absorb bursts by provisioning capacity dynamically, but it does not change how requests map to partitions. If the partition key design causes a single partition key value to receive a disproportionate share of traffic, throttling behavior can still appear for that hot partition even in on-demand mode.
When this WOULD be correct
This option would be correct in a scenario where the application experiences unpredictable, sudden traffic spikes across all partitions (e.g., viral social media campaign), and the issue is overall throughput capacity rather than a single hot key. On-demand capacity automatically scales to handle such spikes without manual provisioning.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The SAA-C03 exam frequently reuses these exact scenarios with slightly different constraints.
✓Add a salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp.Correct answer▾
Why this is correct
Hot partition throttling usually occurs when too many requests target a single partition key value. Salting transforms the partition key so that one high-traffic customerId maps to multiple distinct partition keys (e.g., customerId#0, customerId#1, etc.), which increases the number of partitions that can serve that customer’s workload concurrently and reduces the probability that a single partition becomes overloaded.
✗Increase the table’s provisioned read capacity units while keeping partition key = customerId.Wrong answer — click to see why▾
Why this is wrong here
Increasing read capacity units does not address the root cause: a single hot partition. The total capacity may be sufficient, but all reads for the hot customer hit the same partition, causing throttling at that partition level.
★ When this WOULD be the correct answer
This option would be correct if the question described a scenario where overall table throughput is insufficient due to uniformly high traffic across all partitions, and the goal is simply to increase total capacity without a hot key issue.
Why candidates choose this
Candidates often assume that insufficient capacity is the problem and that increasing RCUs will solve throttling, overlooking that DynamoDB throttles at the partition level, not the table level.
✗Remove the sort key and use timestamp as the partition key to increase cardinality.Wrong answer — click to see why▾
Why this is wrong here
Changing the partition key to timestamp would cause all reads for a given time range to hit a single partition, creating a hot key and worsening the distribution issue, not solving it.
★ When this WOULD be the correct answer
If the question described a scenario where the access pattern is time-series with uniform read traffic across all timestamps (e.g., reading all records from the last hour for analytics), and the issue was low cardinality of the original partition key, then using timestamp as partition key could improve distribution.
Why candidates choose this
Candidates may think that increasing partition key cardinality always improves distribution, overlooking that timestamp as a partition key can create hot partitions for recent data.
✗Switch to on-demand capacity and rely on DynamoDB to automatically distribute reads across partitions.Wrong answer — click to see why▾
Why this is wrong here
On-demand capacity handles throughput spikes but does not address the root cause: a single hot partition. The ProvisionedThroughputExceeded errors occur because one customer's data is concentrated on one partition, and on-demand capacity does not redistribute data across partitions.
★ When this WOULD be the correct answer
This option would be correct in a scenario where the application experiences unpredictable, sudden traffic spikes across all partitions (e.g., viral social media campaign), and the issue is overall throughput capacity rather than a single hot key. On-demand capacity automatically scales to handle such spikes without manual provisioning.
Why candidates choose this
Candidates may think on-demand capacity solves all throughput issues because it eliminates the need to manage capacity, overlooking that hot partitions are a data distribution problem that capacity alone cannot fix.
Analysis generated from the official SAA-C03blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse total table capacity with per-partition capacity, assuming that increasing RCUs or switching to on-demand will fix throttling caused by a hot key, when in reality the bottleneck is the single partition's throughput limit.
Detailed technical explanation
How to think about this question
DynamoDB uses the partition key's hash value to assign items to physical partitions, each with a maximum throughput of 3000 RCUs and 1000 WCUs. By salting the partition key with a deterministic value (e.g., a hash of requestId modulo N), the same customer's data is spread across N partitions, allowing aggregate read throughput to exceed the single-partition limit. This technique is commonly used for high-traffic entities like popular users or products in gaming or e-commerce applications.
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: Add a salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp. — Option B is correct because adding a salt to the partition key (e.g., customerId#hash(requestId)) distributes the read-heavy customer's data across multiple physical partitions. This prevents a single hot partition from throttling requests, even when the table's total provisioned capacity is sufficient. DynamoDB's partition key determines the internal hash used for data placement, so increasing partition key cardinality directly improves read distribution.
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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