SAA-C03 Design High-Performing Architectures Practice Question
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?
⚠ Common exam trap
Many exam-takers 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.
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.
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.
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.
- ✗
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?”
Go deeper
Related to this question
About these practice questions
One of 302 original SAA-C03 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAA-C03 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAA-C03 exam.