Cloud Digital Leader Scaling with Google Cloud operations Practice Question
A company wants to automatically scale their Compute Engine managed instance group based on the number of requests per second. Which metric should they use?
⚠ Common exam trap
Many exam-takers confuse 'HTTP load balancing serving capacity' with request rate, but that metric measures the load balancer's backend capacity utilization (a ratio), not the raw number of requests per second, which requires a custom application metric.
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
✓
custom metric from Cloud Monitoring
The company needs to scale based on requests per second, which is a custom application-level metric. Cloud Monitoring allows you to create custom metrics from your application, and managed instance groups can use these custom metrics for autoscaling, enabling precise scaling based on actual request throughput rather than proxy indicators.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CPU utilization
Why it's wrong here
CPU utilization is an indirect measure of actual request processing. It averages across instances and can lag sharp traffic spikes, failing to capture queued requests or non-CPU-bound work, so scaling on CPU alone may either over- or under-provision capacity relative to the true incoming request volume.
- ✗
HTTP load balancing serving capacity
Why it's wrong here
HTTP load balancing serving capacity reflects the load balancer's operational limits, such as backend connection or QPS ceilings, rather than the rate of incoming requests. This metric warns about backend saturation from the infrastructure perspective, but it is not a clean per-request scaling signal and can trigger on architecture bottlenecks rather than application demand.
- ✗
Instance group size
Why it's wrong here
Instance group size is the result of autoscaling decisions, not a causal metric. Using it as a scaling signal creates a feedback loop: if the group is at its current size, adding more instances would change the size, but the autoscaler would see no independent change in demand—so scaling would never be triggered based on actual workload changes.
- ✓
custom metric from Cloud Monitoring
Why this is correct
A custom metric from Cloud Monitoring can capture application-level signals like requests per second or queue depth, which are directly proportional to user demand. Exporting such a metric via the Cloud Monitoring API and configuring an autoscaling policy on it allows precise, workload-aware scaling that reflects the true load pattern rather than relying on incidental infrastructure indicators.
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
Proxy
A proxy is an intermediary server that sits between a client and a destination server, forwarding requests and responses while providing security, privacy, and control.
Key term
Alerting policy
An alerting policy is a set of rules that defines when to send notifications about a system condition that needs attention.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.