The answer is to enable autoscale on the Cosmos DB container with a maximum throughput limit. This directly addresses the variable workload pattern described, where peak shopping hours cause write throttling (429 errors) and high latency, while off-peak periods would waste resources with fixed high throughput. Autoscale dynamically scales request units (RU/s) between 10% and the set maximum, ensuring sufficient capacity during spikes without over-provisioning during low traffic. On the AZ-204 exam, this scenario tests your understanding of Cosmos DB scaling strategies versus compute scaling—a common trap is confusing App Service scaling with database throughput optimization. Remember that read-heavy issues point to caching, but write throttling and latency point to RU capacity. The key memory tip: “Autoscale for variable writes, fixed for steady reads.”
AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions
This AZ-204 practice question tests your understanding of monitor, troubleshoot, and optimize azure solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are a developer for a large e-commerce company. The company has a global customer base and runs a critical web application on Azure App Service (Premium v3 plan) deployed in multiple regions. The application uses Azure Cosmos DB (multi-region writes enabled) for product catalog and session state. Recently, the operations team reported that during peak shopping hours (e.g., Black Friday), the application becomes slow and some users experience timeouts. You have implemented Application Insights to collect telemetry. After analyzing the data, you find that the Cosmos DB write operations are experiencing high latency (average 200ms) and occasional throttling (429 errors). The read latency is acceptable. The App Service instances are scaled out to 20 instances during peak, and CPU usage is around 70%. You need to optimize the solution to reduce write latency and eliminate throttling without over-provisioning resources. The solution must be cost-effective and require minimal code changes. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable autoscale on the Cosmos DB container with a maximum throughput limit
Option C is correct because enabling Cosmos DB request unit (RU) autoscale allows the database to automatically scale up throughput during peak times, preventing throttling. This is cost-effective when traffic is variable. Option A is wrong because scaling up the App Service plan may help CPU but does not address Cosmos DB throttling. Option B is wrong because increasing RU/s manually could lead to over-provisioning during off-peak. Option D is wrong because enabling Azure Cache for Redis would reduce read latency, not write latency.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Scale up the App Service plan to a higher tier to increase CPU capacity
Why it's wrong here
CPU is at 70%, and the bottleneck is Cosmos DB, not compute.
✗
Implement Azure Cache for Redis to cache Cosmos DB read and write operations
Why it's wrong here
Caching helps reads, not writes; writes would still hit Cosmos DB.
✗
Increase the provisioned RU/s manually before peak hours and decrease after
Why it's wrong here
Manual scaling is not cost-effective and requires operational overhead.
✓
Enable autoscale on the Cosmos DB container with a maximum throughput limit
Why this is correct
Autoscale dynamically adjusts RU/s based on demand, preventing throttling.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-204 question in full detail.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AZ-204 NAT questions on configuration and troubleshooting.
Monitor, troubleshoot, and optimize Azure solutions — This question tests Monitor, troubleshoot, and optimize Azure solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Enable autoscale on the Cosmos DB container with a maximum throughput limit — Option C is correct because enabling Cosmos DB request unit (RU) autoscale allows the database to automatically scale up throughput during peak times, preventing throttling. This is cost-effective when traffic is variable. Option A is wrong because scaling up the App Service plan may help CPU but does not address Cosmos DB throttling. Option B is wrong because increasing RU/s manually could lead to over-provisioning during off-peak. Option D is wrong because enabling Azure Cache for Redis would reduce read latency, not write latency.
What should I do if I get this AZ-204 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AZ-204 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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