Connect to and consume Azure services and third-party services →mediumMultiple ChoiceObjective-mapped

AZ-204 Practice Question: Connect to and consume Azure services and third-party services

Exhibit

Refer to the exhibit.

{
  "version": "1.0",
  "aggregation": {
    "aggregationInterval": "00:05:00",
    "aggregationType": "Average"
  },
  "effectiveStartTime": "2023-01-01T00:00:00Z",
  "effectiveEndTime": "2023-12-31T23:59:59Z",
  "metrics": [
    {
      "name": "MemoryPercent",
      "displayName": "Memory Usage",
      "unit": "Percent",
      "aggregationType": "Average",
      "dimensions": []
    }
  ]
}

Refer to the exhibit. You are configuring Azure Monitor autoscale for a virtual machine scale set using the above JSON metric configuration. The autoscale rule is supposed to scale out when average memory usage exceeds 80%. However, autoscale is not triggering even when memory usage is consistently above 90%. What is the most likely cause?

⚠ Common exam trap

Test-takers frequently assume defining a metric in the configuration automatically creates a scaling rule, but Azure requires an explicit rule condition to link the metric to a scale action.

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 autoscale rule condition is not configured to use this metric.

The exhibit shows a metric configuration, but the autoscale rule itself must explicitly reference that metric in its condition. Without a rule condition that uses this metric, autoscale will not evaluate it, regardless of how the metric is configured. The JSON snippet only defines the metric source, not the scaling rule logic.

Answer analysis

Option-by-option breakdown

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

✗
The aggregation interval is too long; it should be set to 1 minute.
Why it's wrong here
While Azure Monitor metrics often have a 1-minute granularity, an aggregation interval of 5 minutes is a standard and acceptable configuration for autoscale rules. This interval helps to smooth out transient metric spikes, preventing 'flapping' where resources are rapidly scaled up and down due to momentary fluctuations. For many applications, a 5-minute average provides a stable and reliable signal for making scaling decisions without being 'too long' to react effectively.
✗
The metric name is incorrect; it should be 'Percentage Memory'.
Why it's wrong here
The metric name 'MemoryPercent' is a standard, system-defined metric provided by Azure Monitor for various compute resources like Virtual Machines and App Services. Azure platform metrics adhere to specific naming conventions, often using camelCase, and 'Percentage Memory' is not the correct or recognized name for this particular metric. Attempting to use an incorrect metric name would prevent the autoscale rule from properly identifying and monitoring the intended resource utilization.
✗
The aggregation type should be 'Maximum' instead of 'Average'.
Why it's wrong here
Using 'Average' as the aggregation type for a metric like memory utilization is a common and often recommended practice for autoscale rules. It provides a smoothed representation of resource usage over the specified time grain, making the scaling decisions more robust against short-lived spikes that might not warrant immediate scaling. While 'Maximum' could be used, 'Average' typically offers a more balanced approach, reducing the likelihood of unnecessary scale-out actions triggered by brief, non-critical peaks.
✓
The autoscale rule condition is not configured to use this metric.
Why this is correct
The exhibit might demonstrate the successful definition or collection of a custom metric, but this alone does not automatically link it to an autoscale action. For Azure Autoscale to react to any metric, whether platform or custom, a specific autoscale rule must be explicitly configured within an autoscale setting. This rule must reference the exact metric name, its aggregation type, time grain, operator, and a threshold to define the conditions under which scaling actions should occur.

Go deeper

AZ-204 Practice Question: Connect to and consume Azure services and third-party services

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question