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Difficulty: MediumConfigure Azure Monitor Autoscale Rules and Metrics

You are configuring Azure Monitor autoscale rules for several Azure resources. Match each workload scaling requirement on the left to the most appropriate Azure Monitor autoscale metric and time aggregation configuration on the right.

  • A queue-processing backend application needs to scale out rapidly when a large burst of messages is placed in the queue, focusing on the peak volume.Metric: ActiveMessageCount; Time Aggregation: Maximum
  • A memory-intensive microservice running on an Azure App Service plan must scale out when the overall memory usage across all instances exceeds a defined threshold.Metric: MemoryPercentage; Time Aggregation: Average
  • A web API hosted on an Azure App Service plan needs to scale out when requests are queued up due to server thread exhaustion.Metric: HttpQueueLength; Time Aggregation: Average
  • A data-streaming service running on an Azure Virtual Machine Scale Set (VMSS) needs to scale out based on the total volume of network traffic sent from the scale set.Metric: Network Out; Time Aggregation: Total

Answer

Match the queue-processing application with ActiveMessageCount (Maximum); match the memory-intensive microservice with MemoryPercentage (Average); match the web API thread exhaustion with HttpQueueLength (Average); and match the data-streaming service with Network Out (Total).
The correct pairings align each resource requirement with its target resource metric and the mathematically appropriate time aggregation. ActiveMessageCount (Maximum) reacts immediately to queue spikes; MemoryPercentage (Average) tracks memory usage stably; HttpQueueLength (Average) detects IIS/App Service request queuing; and Network Out (Total) measures the total volume of data transmitted over the monitoring window.

Step-by-Step Solution

1
Analyze the queue-processing requirement.
Since it processes messages from a queue and needs to scale out based on peak volume spikes, ActiveMessageCount with a Time Aggregation of Maximum is the correct configuration.
Maximum aggregation captures the peak value within the evaluation interval, ensuring the scale-out action is triggered immediately during a burst.
2
Analyze the memory-intensive microservice requirement.
For memory usage on App Service, the MemoryPercentage metric with an Average time aggregation is appropriate.
Average resource aggregation provides a stable indicator of overall memory consumption, avoiding premature scaling based on brief spikes.
3
Analyze the web API thread exhaustion scenario.
Thread pool exhaustion in an App Service results in requests waiting in the queue. The HttpQueueLength metric aggregated as Average is the correct match.
HttpQueueLength measures the queue of requests waiting to be handled, indicating that the existing instances cannot handle the concurrency load.
4
Analyze the data-streaming service requirement.
Data-streaming outbound volume is monitored using the Network Out metric with a Total time aggregation.
Total time aggregation measures the cumulative bytes sent over the time grain, which represents the total volume of network traffic.

Key Concept

Selecting correct metrics and time aggregation types for Azure Monitor autoscale rules.
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Topics

Configure Azure Monitor Autoscale Rules and Metrics

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