The answer is to implement write sharding by adding a random suffix to the partition key. This is correct because the throttling stems from a hot partition: with a simple primary key and heavily skewed write patterns, a few partitions absorb all the traffic, exceeding their individual capacity even though the table uses on-demand mode. Write sharding distributes those writes evenly across all partitions by randomizing the key, eliminating the hot spot and reducing ThrottledWriteEvents. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding that on-demand capacity handles overall throughput but cannot prevent partition-level throttling from skewed access—a common trap where candidates mistakenly assume on-demand solves all throttling. The key insight is that DynamoDB’s 10 GB partition limit and 3,000 RCU / 1,000 WCU per-partition ceilings still apply. Memory tip: “Shard the hot key to spread the heat.”
DBS-C01 Monitoring and Troubleshooting Practice Question
This DBS-C01 practice question tests your understanding of monitoring and troubleshooting. 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.
A company runs a production Amazon DynamoDB table with on-demand capacity. The table stores session data for a web application. Recently, users have reported occasional slow response times. The operations team notices that the table's ConsumedWriteCapacityUnits metric shows occasional spikes that exceed the provisioned throughput (though on-demand auto-scales), and ThrottledWriteEvents metrics show occasional throttling. The application uses the AWS SDK with default retry logic. The database specialist is asked to investigate. Upon reviewing the table configuration, the specialist finds that the table has a simple primary key (partition key only) and the data access pattern is heavily skewed toward a small number of partition keys. The application writes in batches of 25 items using the BatchWriteItem API. What should the specialist recommend to reduce throttling and improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement write sharding by adding a random suffix to the partition key to distribute writes more evenly.
The correct answer is A because the throttling is caused by a hot partition: the table uses a single partition key, and writes are heavily skewed toward a few keys. By adding a random suffix to the partition key (write sharding), the writes are distributed evenly across all partitions, eliminating hot spots and reducing throttling. The on-demand capacity mode already handles overall throughput, but it cannot prevent throttling on individual partitions when access is skewed.
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.
✓
Implement write sharding by adding a random suffix to the partition key to distribute writes more evenly.
Why this is correct
Write sharding spreads writes across multiple partitions, reducing throttling.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the provisioned read capacity units to handle the load.
Why it's wrong here
Throttling is on writes, not reads; increasing read capacity does not help.
✗
Switch the table to provisioned capacity mode and increase write capacity.
Why it's wrong here
On-demand already auto-scales; the issue is hot partitions, not overall capacity.
✗
Enable DynamoDB Accelerator (DAX) to cache write operations.
Why it's wrong here
DAX caches reads, not writes; it does not reduce write throttling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume on-demand capacity mode eliminates all throttling, but it only manages total table throughput, not per-partition limits, so hot keys still cause throttling.
Detailed technical explanation
How to think about this question
DynamoDB partitions data based on the partition key's hash value; when a small number of keys receive the majority of writes, those partitions hit their individual throughput limits (3,000 RCU or 1,000 WCU per partition) even if the table's total capacity is sufficient. Write sharding uses a random or calculated suffix to spread writes across many logical partition keys, which map to different physical partitions, effectively parallelizing the write load. This technique is especially important for session tables where a few active users can create a hot key.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Monitoring and Troubleshooting — This question tests Monitoring and Troubleshooting — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement write sharding by adding a random suffix to the partition key to distribute writes more evenly. — The correct answer is A because the throttling is caused by a hot partition: the table uses a single partition key, and writes are heavily skewed toward a few keys. By adding a random suffix to the partition key (write sharding), the writes are distributed evenly across all partitions, eliminating hot spots and reducing throttling. The on-demand capacity mode already handles overall throughput, but it cannot prevent throttling on individual partitions when access is skewed.
What should I do if I get this DBS-C01 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: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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