Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
Your organization runs a customer-facing web application on a managed instance group of Compute Engine VMs behind an HTTP(S) load balancer. The monthly bill shows that the VMs are running at only 15% average CPU utilization, yet the team insists they need the current number of VMs to handle peak traffic. You want to reduce compute costs without risking performance during traffic spikes. What should you do?
⚠ Common exam trap
The trap here is assuming that committed use discounts or smaller machine types automatically optimize cost, when the real issue is the fixed number of instances that never scales down.
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
✓
Enable autoscaling on the managed instance group based on CPU utilization, and set the minimum number of instances to a lower value.
The managed instance group is overprovisioned for average load but sized for peak. Autoscaling with a lower minimum lets the group shrink during low demand and expand during spikes, aligning cost with actual usage. The other options either reduce peak capacity, lock in excess capacity, or remove redundancy, none of which solve the cost problem while preserving performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Move the application to a single large Compute Engine instance to reduce the number of VMs.
Why it's wrong here
Consolidating onto one large instance removes the redundancy and horizontal scalability of the managed instance group. It creates a single point of failure and cannot handle peak traffic as flexibly as a group. This approach increases risk and does not provide automatic cost optimization based on demand.
- ✗
Purchase committed use discounts for all current VM instances for a one-year term.
Why it's wrong here
Committed use discounts lower the price per VM but do not reduce the number of VMs running. Since the workload is only at 15% average utilization, you would still pay for excess capacity, just at a discounted rate. This does not right-size the fleet and locks you into the current overprovisioned state for a year.
- ✓
Enable autoscaling on the managed instance group based on CPU utilization, and set the minimum number of instances to a lower value.
Why this is correct
Autoscaling adjusts the number of VM instances in the group based on load, so you pay only for what you need. Setting a lower minimum reduces cost during low-traffic periods, while the autoscaler adds instances when CPU or other metrics rise, preserving performance during peaks. This directly addresses the low average utilization without manual intervention.
- ✗
Change the machine type of all instances to a smaller size that matches the average CPU utilization.
Why it's wrong here
Resizing to a smaller machine type reduces capacity for every instance, including during peak traffic. The team stated they need the current number of VMs to handle peaks, so reducing per-VM capacity would likely cause performance degradation or outages. It also does not address the fact that the total number of VMs is oversized during off-peak hours.
Go deeper
Related to this question
Learn chapter
Google Cloud Resource Hierarchy and Organization
Key term
Managed instance group
A managed instance group is a collection of identical virtual machine instances that are automatically managed as a single unit to ensure high availability and scalability.
Key term
Organization
An Organization is a top-level container in Google Cloud that represents your company or entities and serves as the root node for all your cloud resources, policies, and access control.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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