AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions
You have an Azure Cosmos DB container with a high number of physical partitions. You observe that some partitions are hitting the request unit (RU) limit while others are underutilized. What should you do to better distribute the workload?
⚠ Common exam trap
Test-takers frequently confuse throughput scaling (increasing RU/s) with workload distribution, mistakenly believing that adding more RU/s will fix a hot partition, when in fact the partition's individual RU ceiling remains unchanged.
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
✓
Choose a different partition key that evenly distributes workload
The root cause of uneven RU consumption is a poorly chosen partition key that creates a hot partition. By selecting a partition key with high cardinality and even distribution, you ensure that requests and storage are spread uniformly across all physical partitions, preventing any single partition from throttling while others remain idle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more composite indexes
Why it's wrong here
Adding more composite indexes primarily optimizes query performance for specific filter conditions and sort orders, reducing RUs for those particular read operations. While indexes can improve query efficiency, they do not inherently redistribute data or workload across logical partitions. In fact, more indexes can increase the RU cost for write operations, as the index needs to be updated, potentially exacerbating performance issues if writes are concentrated on a few hot partitions.
- ✗
Increase the total provisioned throughput
Why it's wrong here
Increasing the total provisioned throughput for the container allocates more Request Units per second (RU/s) to the entire resource. However, this throughput is distributed evenly across physical partitions. If the workload is unevenly distributed among logical partitions, and thus concentrated on a few physical partitions (hot partitions), increasing the total throughput will not resolve the bottleneck on those specific hot partitions, as they will still be limited by their allocated share of the total RUs.
- ✓
Choose a different partition key that evenly distributes workload
Why this is correct
Selecting a different partition key that ensures an even distribution of data and request volume across logical partitions is the most effective solution for addressing uneven workload. A good partition key choice, such as one with high cardinality and even access patterns, prevents "hot partitions" where a disproportionate amount of requests or data are directed to a single logical partition. This allows Cosmos DB to scale throughput horizontally and efficiently, ensuring that the provisioned RUs are utilized effectively across all physical partitions.
- ✗
Change the default consistency level to eventual
Why it's wrong here
Changing the default consistency level to eventual primarily impacts the latency and staleness guarantees for read operations, and the trade-off between consistency, availability, and performance. It determines how quickly a write operation is visible to subsequent reads across replicas. However, consistency levels have no bearing on how data is physically distributed across logical or physical partitions, nor do they influence the distribution of request volume across those partitions.
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Senior Network & Security Engineer · founder of Courseiva
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