The answer is to use appropriate indexes to support common query patterns and to shard data based on access patterns. Indexes are critical in DocumentDB design best practices because they allow the query engine to quickly locate documents without scanning entire collections, directly reducing I/O and latency. Sharding, or partitioning data across cluster instances using a well-chosen shard key aligned with your access patterns, distributes read and write load evenly, preventing hot partitions and enabling cost-efficient horizontal scaling. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding that DocumentDB is a managed MongoDB-compatible service where index design and shard key selection are the primary levers for performance and cost—not vertical scaling alone. A common trap is assuming more indexes always help; in reality, over-indexing increases write overhead and storage costs. Memory tip: “Index for reads, shard for writes” reminds you that indexes speed up queries while sharding spreads the write load, together optimizing both performance and cost.
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 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 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.
Sharding data based on access patterns (Option A) improves query performance by distributing read/write load across multiple shards, reducing contention and latency. In Amazon DocumentDB, sharding is achieved through a cluster with multiple instances, and aligning shard keys with access patterns ensures even data distribution and efficient query routing. This also reduces costs by allowing you to scale horizontally only when needed, avoiding over-provisioning of larger instances.
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.
✓
Shard data based on access patterns to distribute load.
Why this is correct
Sharding improves scalability.
Related concept
Read the scenario before looking for a memorised answer.
✗
Design documents to avoid joins by frequently using $lookup.
Why it's wrong here
$lookup is expensive.
✗
Avoid denormalization to maintain strict normal forms.
Why it's wrong here
Denormalization is common in document databases to reduce joins.
✗
Store all documents in a single collection without indexes to reduce overhead.
Why it's wrong here
Lack of indexes hurts query performance.
✓
Use appropriate indexes to support common query patterns.
Why this is correct
Indexes speed up queries.
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 confuse sharding with partitioning in relational databases or assume that avoiding indexes reduces overhead, but in DocumentDB, indexes are critical for performance and sharding is a horizontal scaling strategy that directly impacts cost and query speed.
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
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: Shard data based on access patterns to distribute load. — Sharding data based on access patterns (Option A) improves query performance by distributing read/write load across multiple shards, reducing contention and latency. In Amazon DocumentDB, sharding is achieved through a cluster with multiple instances, and aligning shard keys with access patterns ensures even data distribution and efficient query routing. This also reduces costs by allowing you to scale horizontally only when needed, avoiding over-provisioning of larger instances.
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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