- A
Shard data based on access patterns to distribute load.
Correct: Sharding data based on access patterns distributes load across instances, improving throughput and reducing latency, even if implemented at the application level.
- B
Design documents to avoid joins by frequently using $lookup.
Why wrong: Incorrect: Frequently using $lookup performs joins, which are expensive in document databases. Denormalization is preferred to avoid joins.
- C
Avoid denormalization to maintain strict normal forms.
Why wrong: Avoiding denormalization is contrary to document database best practices. Denormalizing related data into a single document avoids expensive joins and improves read performance, which reduces costs and latency. Therefore, this option is incorrect.
- D
Store all documents in a single collection without indexes to reduce overhead.
Why wrong: Incorrect: Storing documents without indexes forces full collection scans, severely degrading query performance and increasing costs.
- E
Use appropriate indexes to support common query patterns.
Correct: Using appropriate indexes minimizes the amount of data scanned, speeding up queries and reducing I/O costs.
DBS-C01 Sharding (Application-Level) 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. A key principle to apply: sharding (Application-Level). 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 is designing a document database on Amazon DocumentDB for a content management system. Which TWO design practices improve query performance and reduce costs?
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
Shard data based on access patterns to distribute load.
The question asks for two design practices that improve performance and reduce costs. The correct answers are not fully represented in the options; among the given choices, only Option E is correct. However, the second intended correct practice is denormalization—storing related data together in a single document to avoid expensive joins. Option C (avoid denormalization) is wrong because it suggests maintaining strict normal forms, which hurts read performance. Option E is correct because appropriate indexes minimize document scans. Therefore, the two key practices are indexing and denormalization.
Key principle: Sharding (Application-Level)
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Shard data based on access patterns to distribute load.
Why this is correct
Correct: Sharding data based on access patterns distributes load across instances, improving throughput and reducing latency, even if implemented at the application level.
Related concept
Sharding (Application-Level)
- ✗
Design documents to avoid joins by frequently using $lookup.
Why it's wrong here
Incorrect: Frequently using $lookup performs joins, which are expensive in document databases. Denormalization is preferred to avoid joins.
- ✗
Avoid denormalization to maintain strict normal forms.
Why it's wrong here
Avoiding denormalization is contrary to document database best practices. Denormalizing related data into a single document avoids expensive joins and improves read performance, which reduces costs and latency. Therefore, this option is incorrect.
- ✗
Store all documents in a single collection without indexes to reduce overhead.
Why it's wrong here
Incorrect: Storing documents without indexes forces full collection scans, severely degrading query performance and increasing costs.
- ✓
Use appropriate indexes to support common query patterns.
Why this is correct
Correct: Using appropriate indexes minimizes the amount of data scanned, speeding up queries and reducing I/O costs.
Related concept
Sharding (Application-Level)
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often think DocumentDB supports native sharding like MongoDB. In reality, sharding must be done at the application level, but it remains a valid design practice for scaling. Additionally, proper indexing is the primary performance optimization.
Detailed technical explanation
How to think about this question
Under the hood, DocumentDB uses a distributed storage architecture with 6 copies of data across 3 Availability Zones, and sharding (via a sharded cluster) partitions data based on the shard key, enabling parallel query execution across shards. For example, a content management system with high-read patterns on recent documents can use a shard key like `created_at` to isolate hot data, reducing latency and avoiding throttling. Proper index design, such as compound indexes covering filter and sort fields, minimizes the number of documents scanned and leverages in-memory caching.
KKey Concepts to Remember
- Sharding (Application-Level)
- Indexing
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
Sharding (Application-Level)
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.
Review sharding (Application-Level), then practise related DBS-C01 questions on the same topic to reinforce the concept.
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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 — Sharding (Application-Level).
What is the correct answer to this question?
The correct answer is: Shard data based on access patterns to distribute load. — The question asks for two design practices that improve performance and reduce costs. The correct answers are not fully represented in the options; among the given choices, only Option E is correct. However, the second intended correct practice is denormalization—storing related data together in a single document to avoid expensive joins. Option C (avoid denormalization) is wrong because it suggests maintaining strict normal forms, which hurts read performance. Option E is correct because appropriate indexes minimize document scans. Therefore, the two key practices are indexing and denormalization.
What should I do if I get this DBS-C01 question wrong?
Review sharding (Application-Level), then practise related DBS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Sharding (Application-Level)
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Last reviewed: Jun 24, 2026
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