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Difficulty: HardConfigure Azure Monitor Autoscale Rules and Metrics

You are designing autoscale rules for various Azure workloads. Match each workload requirement on the left to the correct Azure Monitor Autoscale configuration pattern on the right to optimize resource scaling and prevent flapping.

  • Prevent transient CPU spikes lasting less than 55 minutes from triggering a scale-out action on an App Service plan.Configure the metric trigger Duration to 1010 minutes and use Average Time Aggregation.
  • Trigger a scale-out action when any individual virtual machine within a Virtual Machine Scale Set (VMSS) exceeds a memory utilization threshold.Set the Metric Statistic (Instance Aggregation) to Maximum for the Memory metric.
  • Avoid rapid scale-out and scale-in oscillations (flapping) when configuring a scale-in rule for a queue-based processing workload.Set a sufficient margin between scale-out and scale-in thresholds, and configure an adequate Cool-down period.
  • Scale out a service based on the cumulative volume of transaction logs generated across all instances during a 1010-minute evaluation window.Set the Time Aggregation to Total for the custom volume metric over the specified time window.

Answer

To prevent transient spikes, configure the duration to 1010 minutes with Average Time Aggregation. To scale based on any individual instance, set the Metric Statistic (Instance Aggregation) to Maximum. To prevent flapping, use a sufficient margin between thresholds and a Cool-down period. To scale based on cumulative volume, set the Time Aggregation to Total.
The correct matches align with standard Azure Monitor autoscale rules: Average Time Aggregation over a 1010-minute window smooths out transient spikes under 55 minutes; setting Metric Statistic to Maximum ensures an individual instance triggering the threshold scales the group; appropriate threshold margins and Cool-down periods prevent flapping; and Total Time Aggregation is used to measure the aggregate volume of metrics over a time window.

Step-by-Step Solution

1
Analyze transient spike prevention requirements.
Identify that short-term fluctuations must be averaged out over a window longer than the spike duration, mapping to 'Configure the metric trigger Duration to 1010 minutes and use Average Time Aggregation'.
This prevents single-minute spikes from skewing the autoscale decision prematurely.
2
Analyze individual instance behavior requirements.
Identify that the metric aggregation across instances (Metric Statistic) must be set to Maximum to catch when any single instance crosses the threshold, mapping to 'Set the Metric Statistic (Instance Aggregation) to Maximum for the Memory metric'.
By default, Azure Monitor averages metrics across all instances. Maximum ensures the highest loaded node triggers scaling.
3
Analyze flapping prevention requirements.
Identify that setting thresholds too close together or using short cool-downs causes loops. Mapping to 'Set a sufficient margin between scale-out and scale-in thresholds, and configure an adequate Cool-down period'.
Cool-down and threshold separation allow the system to reach a steady state before evaluating rules again.
4
Analyze cumulative volume scaling requirements.
Identify that cumulative counts require summing up values, mapping to 'Set the Time Aggregation to Total for the custom volume metric over the specified time window'.
Total aggregation represents the sum of all sample values in the window, capturing cumulative totals.

Key Concept

Azure Monitor Autoscale rule configuration parameters, including Time Aggregation, Metric Statistic (Instance Aggregation), Durations, and Cool-down periods to prevent flapping and optimize scaling behavior.
Estimated Time:2m 30s
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Topics

Configure Azure Monitor Autoscale Rules and Metrics

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