The answer is to evaluate denormalization, appropriate indexing, and write-optimized sharding strategies. This is correct because denormalizing data by embedding nested attributes into single documents eliminates expensive joins, directly supporting high write throughput while enabling DocumentDB’s native document engine to efficiently traverse complex nested queries. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of balancing write performance against query flexibility—a common trap is over-indexing, which slows writes, or normalizing into separate collections, which forces cross-document lookups. Remember that DocumentDB is optimized for document-level operations, so embedding related data and using targeted indexes on frequently queried fields keeps writes fast and queries responsive. A useful memory tip: “Embed for speed, index for need, shard for spread.”
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. The workload requires high write throughput and needs to support complex queries on nested attributes. Which THREE design considerations should the company evaluate to meet these requirements?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Denormalize data to reduce the number of joins.
Option A is correct because denormalizing data in Amazon DocumentDB reduces the need for joins, which are expensive and can degrade write throughput. By embedding related data into a single document, the database can perform complex queries on nested attributes more efficiently, as DocumentDB is optimized for document-level operations. This design aligns with the workload's requirement for high write throughput and complex query support.
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.
✓
Denormalize data to reduce the number of joins.
Why this is correct
Denormalization improves query performance for document databases.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable Multi-AZ deployment for high availability.
Why it's wrong here
Multi-AZ does not improve write throughput or query performance.
✓
Use sharding to distribute write load across shards.
Why this is correct
Sharding increases write throughput by distributing writes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use change streams to capture and process data changes.
Why it's wrong here
Change streams are for data capture, not for improving throughput or queries.
✓
Use appropriate indexes on frequently queried fields.
Why this is correct
Indexes speed up queries on nested attributes.
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 high availability features (like Multi-AZ) with performance optimization, or mistake change streams as a mechanism to improve write throughput rather than a tool for capturing data changes.
Detailed technical explanation
How to think about this question
In Amazon DocumentDB, denormalization leverages the document model to store related data in a single BSON document, allowing atomic writes and efficient nested queries via dot notation. Sharding distributes write load across multiple instances by partitioning data based on a shard key, which is critical for high write throughput in large-scale workloads. Proper indexing on nested fields (e.g., using wildcard indexes or compound indexes) ensures that complex queries on nested attributes avoid full collection scans, maintaining low latency.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Denormalize data to reduce the number of joins. — Option A is correct because denormalizing data in Amazon DocumentDB reduces the need for joins, which are expensive and can degrade write throughput. By embedding related data into a single document, the database can perform complex queries on nested attributes more efficiently, as DocumentDB is optimized for document-level operations. This design aligns with the workload's requirement for high write throughput and complex query support.
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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