AZ-204 Develop Azure compute solutions Practice Question
You have an Azure Container Apps environment running multiple microservices. One microservice is experiencing high CPU usage and slow response times. You need to configure autoscaling rules to scale based on HTTP requests. Which scaling rule should you add?
⚠ Common exam trap
Many exam-takers choose CPU or memory scaling rules because they seem directly related to high CPU usage, but the question explicitly asks for scaling based on HTTP requests, which requires a request-based scaler like KEDA's HTTP scaler, not resource-based metrics.
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
✓
HTTP scaling rule (KEDA)
KEDA's HTTP scaling rule is specifically designed to scale Azure Container Apps based on the number of concurrent HTTP requests, which directly addresses high CPU usage and slow response times caused by request load. Unlike CPU or memory metrics, HTTP scaling reacts to incoming request volume proactively, allowing the microservice to handle spikes before resource saturation occurs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
HTTP scaling rule (KEDA)
Why this is correct
An HTTP scaling rule, powered by KEDA (Kubernetes Event-driven Autoscaling), is the most appropriate choice for web microservices in Azure Container Apps. This rule directly monitors the rate of incoming HTTP requests or the number of concurrent requests per replica, allowing the container app to scale out proactively as web traffic increases. This direct correlation ensures rapid and efficient response to fluctuating demand, maintaining optimal performance and availability for user-facing applications.
- ✗
CPU percentage scaling rule
Why it's wrong here
A CPU percentage scaling rule is generally not the most effective strategy for immediately responding to HTTP request spikes in web microservices. While increased requests can eventually lead to higher CPU utilization, CPU is a lagging indicator; the application might already be experiencing performance degradation or latency before the CPU threshold is consistently met. This reactive approach can result in slower scaling responses during sudden traffic bursts, impacting user experience.
- ✗
Memory percentage scaling rule
Why it's wrong here
A memory percentage scaling rule is unsuitable for dynamically scaling web microservices based on HTTP request bursts. Memory consumption typically reflects the application's data processing, caching, or state management, rather than the immediate volume or rate of incoming requests. Scaling solely on memory would likely be too slow or inaccurate to prevent performance bottlenecks during rapid increases in web traffic, as memory usage doesn't directly correlate with the need for more replicas to handle new connections.
- ✗
Custom scaling rule using Azure Monitor metrics
Why it's wrong here
While custom scaling rules using Azure Monitor metrics offer flexibility, they introduce unnecessary complexity when a highly optimized, built-in HTTP scaler is available. Implementing a custom rule requires defining the specific metric, configuring its collection, and potentially developing a custom KEDA scaler, which adds overhead in development and maintenance. For common web traffic patterns, the native HTTP scaling rule provides a more straightforward, robust, and efficient solution without the additional configuration burden.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-204 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-204 exam.