- 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.
Quick Answer
The answer is to add a salt component to the partition key, such as changing it to customerId#salt derived from a hash of requestId, while keeping the sort key as timestamp. This directly solves DynamoDB hot partition key read throttling because the salt increases partition key cardinality, spreading a single customer’s high read traffic across multiple physical partitions. Without this change, all reads for that customer land on one partition, causing ProvisionedThroughputExceeded errors even when the table’s total capacity is sufficient. On the SAA-C03 exam, this scenario tests your understanding that DynamoDB’s internal hash on the partition key determines data placement, so a low-cardinality key like customerId creates a hot partition. A common trap is assuming that increasing total read capacity units alone fixes the issue, but throttling persists if the hot partition’s individual limit is exceeded. Memory tip: “Salt spreads the heat”—adding a random suffix to the partition key distributes load and prevents a single key from becoming a bottleneck.
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.
- ✓
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.
- ✗
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.
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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