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AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions

Your application running on Azure App Service is experiencing intermittent high latency. You have enabled Application Insights and noticed that the 'Server response time' metric spikes during peak hours. What is the most likely cause of this issue?

⚠ Common exam trap

A common mix-up: candidates confuse high memory usage with CPU saturation; memory pressure causes different symptoms (e.g., 500 errors, restarts) while CPU limits directly manifest as increased response times, making option D a distractor.

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

The App Service plan is under-provisioned and hitting CPU limits.

Intermittent high latency during peak hours, reflected in the 'Server response time' metric, typically indicates that the App Service plan is under-provisioned. When CPU usage hits the plan's limits, requests queue up and response times increase. Auto-scaling would mitigate this, but if the plan is under-provisioned (e.g., a B1 plan with limited cores), scaling out may not occur quickly enough or may be disabled, causing CPU saturation and latency spikes.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Auto-scaling is configured too aggressively.

    Why it's wrong here

    Aggressive auto-scaling is designed to proactively add more instances to an App Service plan when demand increases, thereby preventing resource exhaustion and mitigating latency spikes. If anything, an aggressively configured auto-scaling rule would reduce, not increase, application latency during peak loads by ensuring sufficient compute capacity is always available. Therefore, it would not be the cause of increased response times.

  • The application is using regional failover, causing delays.

    Why it's wrong here

    Regional failover is a high-availability and disaster recovery strategy that redirects traffic to a geographically separate region in the event of a catastrophic outage in the primary region. This mechanism is not active during normal operations and does not cause intermittent performance delays or latency spikes within a single region. Its purpose is to ensure business continuity during widespread regional failures, not to manage routine performance fluctuations.

  • The App Service plan is under-provisioned and hitting CPU limits.

    Why this is correct

    When an App Service plan is under-provisioned, its allocated CPU resources may become fully saturated during periods of high demand, such as peak hours. Hitting CPU limits means the server cannot process new requests immediately, leading to a backlog in the request queue. This directly results in significantly increased application response times and observable latency spikes, as requests must wait for CPU cycles to become available, indicating a need for scaling up or out.

  • The application is using too much memory.

    Why it's wrong here

    While excessive memory consumption can lead to performance degradation, such as increased garbage collection cycles, application restarts due to out-of-memory errors, or disk thrashing if memory limits are exceeded, it is less commonly the direct cause of intermittent latency spikes during peak load compared to CPU saturation. CPU limits directly bottleneck request processing, causing immediate queuing and increased response times, whereas memory issues often manifest differently, like crashes or sustained slowness.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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