DBS-C01 Workload-Specific Database Design Practice Question
A company is running a MongoDB-compatible workload on Amazon DocumentDB. They are experiencing high write latency during peak hours. The current cluster has one instance (db.r5.large) with 100 GB storage. Which change is most likely to improve write performance?
⚠ Common exam trap
A common mix-up: candidates assume increasing storage or adding read replicas will improve write performance, but in DocumentDB, write throughput is limited by the primary instance's compute resources, not by storage size or read capacity.
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
✓
Increase the instance size to db.r5.xlarge
Increasing the instance size to db.r5.xlarge provides more CPU and memory resources, which directly improves the cluster's ability to handle write operations under load. In Amazon DocumentDB, write performance is primarily bound by the instance's compute capacity (vCPUs and memory) for processing write requests and managing the storage engine's buffer cache. A larger instance reduces contention and allows more concurrent writes to be processed efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase storage to 500 GB
Why it's wrong here
Storage scaling does not directly improve write latency; DocumentDB storage is automatically scaled.
- ✗
Enable Multi-AZ deployment
Why it's wrong here
Multi-AZ provides high availability but does not improve write performance.
- ✓
Increase the instance size to db.r5.xlarge
Why this is correct
A larger instance provides more CPU and memory, which can improve write performance.
- ✗
Add a read replica in a different Availability Zone
Why it's wrong here
Read replicas handle read traffic only, not writes.
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