DBS-C01 Workload-Specific Database Design Practice Question
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?
⚠ Common exam trap
Many candidates 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.
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
✓
Denormalize data to reduce the number of joins.
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.
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.
- ✗
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.
- ✗
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.
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