AZ-204 Develop Azure compute solutions Practice Question
Which TWO actions should you perform to configure autoscaling for an Azure App Service web app based on CPU usage?
⚠ Common exam trap
It's easy for candidates to confuse the 'default instance count' (Option C) with the minimum instance limit, but the default count is only a starting point and does not define the scaling range, whereas the minimum and maximum limits are mandatory for autoscaling configuration.
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
✓
Configure the minimum and maximum instance limits.
Option A is correct because autoscaling requires you to set the minimum and maximum instance limits, which define the boundary of the instance count that the autoscale engine can scale between. Option E is correct because the scenario specifically requires scaling based on CPU usage, so you must define a scale rule with a metric trigger that fires when CPU percentage exceeds a chosen threshold. Option B is incorrect because manual scaling fixes the instance count and does not provide automatic, metric-driven scaling. Option C is incorrect because setting a default instance count does not by itself create an autoscale policy or CPU-based rule. Option D is incorrect because a date/time-based rule is a schedule-based trigger, not a CPU-usage-based trigger.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the minimum and maximum instance limits.
Why this is correct
Configuring minimum and maximum instance limits is fundamental for any autoscaling profile. These limits define the operational boundaries, ensuring that the application always maintains a baseline availability while preventing uncontrolled resource consumption and associated costs. Without these explicit boundaries, an autoscaling system cannot effectively manage its scale-out and scale-in operations.
- ✗
Enable manual scaling and set the instance count to 3.
Why it's wrong here
Enabling manual scaling directly contradicts the principle of autoscaling, which dynamically adjusts instance counts based on demand. Manual scaling fixes the number of instances to a static value, such as 3, requiring an administrator to manually intervene for any changes. This approach offers no automatic responsiveness to fluctuating workloads, making it unsuitable for an autoscaling configuration.
- ✗
Set the default instance count to 1.
Why it's wrong here
Setting a default instance count merely establishes the initial number of instances when the service starts or when no active scaling rules apply. While a necessary baseline, it does not define the dynamic behavior or conditions under which the system scales up or down. Autoscaling requires explicit rules and thresholds to react to metrics, not just a static default.
- ✗
Create a scale rule based on a specific date and time.
Why it's wrong here
Creating a scale rule based on a specific date and time configures scheduled scaling, which adjusts instance counts at predefined intervals or specific times. While a form of automated scaling, it is distinct from reactive, metric-based autoscaling that responds to real-time load fluctuations like CPU usage. This action does not configure dynamic scaling based on performance metrics.
- ✓
Define a scale rule that triggers when CPU percentage exceeds a threshold.
Why this is correct
Defining a scale rule that triggers when CPU percentage exceeds a threshold is a core component of metric-based autoscaling. This rule specifies the performance indicator (CPU), the condition (exceeds a threshold), and the resulting action (scale-out). It enables the application to dynamically react to increased demand by provisioning more resources, ensuring performance and availability.
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