AZ-204 Develop Azure compute solutions Practice Question
A company runs a critical web app on Azure App Service that must handle traffic spikes without downtime. They set up autoscaling rules based on CPU percentage. However, during a spike, the app becomes unresponsive before new instances are added. What should they do?
⚠ Common exam trap
It's easy for candidates to assume reactive autoscaling (e.g., lowering thresholds or changing metrics) can solve latency issues, but they overlook the fundamental cold-start delay that requires proactive instance pre-warming.
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
✓
Use pre-warming instances with a scheduled scaling rule
Pre-warming instances with a scheduled scaling rule ensures that additional instances are already running and ready to handle traffic before the CPU spike occurs. This avoids the cold-start delay inherent in reactive autoscaling, where new instances take time to provision and initialize, causing unresponsiveness during rapid 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.
- ✗
Switch to memory-based autoscaling
Why it's wrong here
Switching to memory-based autoscaling would be ineffective if the primary performance bottleneck during high load is CPU utilization, as indicated by the problem context. If the application's CPU resources are exhausted under heavy traffic, scaling based on memory metrics would not trigger additional instances when needed, allowing the CPU to remain saturated and user experience to degrade. This misaligned scaling metric would fail to address the root cause of the performance issues.
- ✗
Decrease the scale-in cooldown period
Why it's wrong here
Decreasing the scale-in cooldown period is counterproductive for addressing performance issues during anticipated load spikes. The scale-in cooldown dictates how long an instance must remain below the scale-in threshold before it can be removed, aiming to prevent "flapping." Shortening this period would make the system more aggressive in reducing instance count, which is detrimental when preparing for or experiencing high demand, potentially leading to insufficient capacity and further performance degradation if traffic remains elevated.
- ✓
Use pre-warming instances with a scheduled scaling rule
Why this is correct
Using pre-warming instances with a scheduled scaling rule is the most effective solution for mitigating performance degradation during anticipated load spikes. This approach allows new instances to be added and fully initialized, including application startup and caching, *before* the expected surge in traffic. By having instances ready and "warm" ahead of time, the application can immediately handle the increased load without experiencing cold start delays or performance bottlenecks, ensuring a smooth user experience.
- ✗
Increase the CPU percentage threshold for scale-out
Why it's wrong here
Increasing the CPU percentage threshold for scale-out would exacerbate performance problems during high load spikes. A higher threshold means the autoscaling rule would wait longer for CPU utilization to reach a more critical level before triggering the addition of new instances. This delay in scaling out would result in existing instances becoming severely overloaded for an extended period, leading to slower response times, increased errors, and a significantly degraded user experience during peak demand.
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Senior Network & Security Engineer · founder of Courseiva
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