GCP Professional Cloud Architect Practice Question
A media company runs a regional managed instance group (MIG) of Compute Engine VMs that pull messages from a Pub/Sub subscription and transcode videos. Each VM keeps CPU usage around 30 %. During traffic bursts the Pub/Sub backlog grows quickly, delaying processing. You must scale the MIG automatically so the backlog stays below 1 000 messages without significantly over-provisioning idle vCPUs. Which autoscaling approach best meets this requirement?
Enable predictive autoscaling so the MIG launches additional VMs in advance of traffic spikes.
Configure the MIG autoscaler to track a Cloud Monitoring metric that measures the Pub/Sub subscription's undeliveredāmessage count and scale to keep the value below 1 000.
Attach an external HTTP(S) load balancer in front of the MIG and autoscale on load-balancing utilization at 60 %.
Scale the MIG based on average CPU utilization with a target of 60 %.
Because CPU utilization remains low even when work queues pile up, scaling on CPU or load-balancing utilization would not respond to growing latency. Compute Engine autoscaling can instead watch any Cloud Monitoring metric. By exporting the metric that counts undelivered Pub/Sub messages and setting a target value (for example, 1 000), the autoscaler adds or removes VM instances directly in response to queue depth, keeping latency controlled while avoiding unnecessary instances. Predictive autoscaling needs regular traffic patterns to be effective, which this workload lacks.
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What is a Managed Instance Group (MIG) in Compute Engine?
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How does Cloud Monitoring work with autoscaling in Google Cloud?
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Why is scaling based on Pub/Sub message backlog better than using CPU utilization?
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What is a regional managed instance group (MIG) in Google Cloud?
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How does Pub/Sub work in Google Cloud?
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What is Cloud Monitoring in Google Cloud, and how can metrics be used for autoscaling?
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GCP Professional Cloud Architect
Designing and planning a cloud solution architecture
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