The answer is to add a write sharding pattern by appending a random suffix to the partition key. This resolves DynamoDB write throttling because the throttling stems from a hot partition—a single user_id partition key receiving a disproportionate volume of writes during peak hours. By distributing those writes across multiple partitions via a random suffix, you eliminate the hot spot and balance write capacity without altering the read logic, since the GSI can still be queried with a sort key condition on post_timestamp. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of adaptive capacity versus manual sharding; a common trap is to assume that increasing write capacity units alone fixes throttling, but that fails if the hot partition remains. Remember the memory tip: “Shard the hot key, not the budget.”
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. 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 startup is building a social media application that stores user posts in Amazon DynamoDB. The access pattern is to retrieve posts by user_id (partition key) sorted by post_timestamp (sort key) in descending order. The table has a global secondary index (GSI) with the same key structure but with different projection. The application reads from the GSI. Recently, the team noticed that writes to the base table are throttled during peak hours. The write capacity is balanced across partitions. Which design change should be made to reduce write throttling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a write sharding pattern by appending a random suffix to the partition key.
The correct answer is D because the write throttling is caused by a hot partition, where a single partition key (user_id) receives a disproportionate number of writes. By appending a random suffix to the partition key, the writes are distributed evenly across multiple partitions, eliminating the hot spot. This is a well-known sharding pattern for DynamoDB when access patterns create uneven write traffic, and it does not require changing the read logic because the GSI can be queried with a sort key condition on post_timestamp.
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 DynamoDB Accelerator (DAX) for writes.
Why it's wrong here
DAX caches reads, not writes.
✗
Increase the write capacity units (WCUs) on the base table.
Why it's wrong here
This may help temporarily but does not address the root cause if throttling is due to hot partitions.
✗
Switch to on-demand capacity mode.
Why it's wrong here
On-demand handles spikes but can be costly; it does not address hot partitions.
✓
Add a write sharding pattern by appending a random suffix to the partition key.
Why this is correct
Sharding distributes writes across partitions, reducing hot spots.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing capacity or switching to on-demand mode will solve all throttling issues, but they overlook the fundamental partition-level throughput limits that cause hot partition throttling.
Detailed technical explanation
How to think about this question
DynamoDB partitions data based on the partition key's hash value; each partition has a maximum throughput of 3,000 RCU and 1,000 WCU. When a single user_id receives many writes, that partition's capacity is exhausted, causing throttling. The write sharding pattern appends a random suffix (e.g., 1-10) to the partition key, distributing writes across 10 partitions, each with its own throughput limit. The application can still query all posts for a user by using a GSI with the original user_id as the partition key and post_timestamp as the sort key, or by performing a scatter-gather query across the sharded keys.
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.
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a write sharding pattern by appending a random suffix to the partition key. — The correct answer is D because the write throttling is caused by a hot partition, where a single partition key (user_id) receives a disproportionate number of writes. By appending a random suffix to the partition key, the writes are distributed evenly across multiple partitions, eliminating the hot spot. This is a well-known sharding pattern for DynamoDB when access patterns create uneven write traffic, and it does not require changing the read logic because the GSI can be queried with a sort key condition on post_timestamp.
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.
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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