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Question 294 of 1,730

Workload-Specific Database Design →mediumMultiple ChoiceObjective-mapped

DynamoDB Write Sharding Pattern to Avoid Hot Partitions

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 gaming company runs a global leaderboard on Amazon DynamoDB. The leaderboard is updated frequently and must return the top 100 scores in milliseconds. The current design uses a single table with a Global Secondary Index (GSI) on score. However, the query to retrieve top scores often throttles under load. Which design change would best improve performance?

A
Use a scan operation with a filter to retrieve top scores.
Why wrong: Scan is inefficient for large datasets and does not guarantee top scores without sorting.
B
Implement a write shard pattern using a random suffix on the partition key and a GSI on score.
Sharding distributes write load, and the GSI on score enables efficient range queries for top scores.
C
Add DynamoDB Accelerator (DAX) in front of the table.
Why wrong: DAX caches reads but does not reduce write throttling on the GSI.
D
Switch to strongly consistent reads for the leaderboard query.
Why wrong: Consistency model does not affect throttling.

Full breakdown with real-world context →

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

✓

Implement a write shard pattern using a random suffix on the partition key and a GSI on score.

Option B is correct because the write shard pattern distributes high-frequency writes across multiple partition keys by appending a random suffix, preventing hot partitions. The GSI on score still allows efficient top-N queries by scanning the index in descending order. This avoids throttling by spreading write capacity evenly, while the GSI remains a sparse index that can be queried without impacting the base table's write throughput.

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 a scan operation with a filter to retrieve top scores.
Why it's wrong here
Scan is inefficient for large datasets and does not guarantee top scores without sorting.
✓
Implement a write shard pattern using a random suffix on the partition key and a GSI on score.
Why this is correct
Sharding distributes write load, and the GSI on score enables efficient range queries for top scores.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add DynamoDB Accelerator (DAX) in front of the table.
Why it's wrong here
DAX caches reads but does not reduce write throttling on the GSI.
✗
Switch to strongly consistent reads for the leaderboard query.
Why it's wrong here
Consistency model does not affect throttling.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume caching (DAX) or consistency changes will fix throttling, but the real issue is write-side hot partitions, which the write shard pattern directly addresses by distributing the write load.

Detailed technical explanation

How to think about this question

The write shard pattern uses a random suffix (e.g., a number from 0 to N) appended to the partition key to distribute writes across N partitions, each with its own write capacity. The GSI on score is then queried with a ScanIndexForward=false to retrieve the top scores in descending order; because the GSI is a separate physical index, its read capacity can be provisioned independently. In practice, choosing N too large can scatter writes too thinly, making top-N queries slower because the GSI must merge results from many shards, so a balanced N (e.g., 10–100) is critical.

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

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this DBS-C01 question test?

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: Implement a write shard pattern using a random suffix on the partition key and a GSI on score. — Option B is correct because the write shard pattern distributes high-frequency writes across multiple partition keys by appending a random suffix, preventing hot partitions. The GSI on score still allows efficient top-N queries by scanning the index in descending order. This avoids throttling by spreading write capacity evenly, while the GSI remains a sparse index that can be queried without impacting the base table's write throughput.

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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Same concept, more angles

1 more ways this is tested on DBS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A gaming company uses Amazon DynamoDB with global tables across two regions. They notice increased write latency and throttling during peak hours. The access pattern is mostly writes to a small set of hot partitions. Which design change would best address this?

hard

✓ A.Implement write sharding using a random suffix on the partition key
B.Enable DynamoDB Accelerator (DAX)
C.Switch to DynamoDB on-demand capacity mode
D.Increase write capacity using auto scaling

Why A: The correct answer is A because the issue is hot partitions caused by a small set of partition keys receiving the majority of writes. By implementing write sharding with a random suffix on the partition key, you distribute writes across multiple partitions, reducing throttling and write latency. This directly addresses the root cause of uneven access patterns, unlike the other options that either cache reads, adjust capacity mode, or scale capacity without solving the partition-level bottleneck.

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