Google PCA Practice Question: Analyze and optimize technical and business processes
A media company uses a multi-project Google Cloud organization. They want to optimize their cloud spend across all projects without sacrificing performance or reliability. They have already implemented committed use discounts for Compute Engine. Which two additional actions should the architect recommend to reduce costs? (Choose two.)
⚠ Common exam trap
The trap here is assuming that any cost-related action, such as enabling billing export or buying more commitments, will reduce spend, when only actions that change resource usage or storage class directly lower the bill.
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
✓
Implement automatic resource scheduling to shut down non-production VMs during off-hours.
Shutting down non-production VMs during off-hours directly cuts compute costs without impacting production reliability. Moving infrequently accessed data to Nearline or Coldline storage reduces storage costs while maintaining low-latency access when needed. Both actions are practical, low-risk optimizations that address different parts of the bill. Visibility tools and preemptible VMs for production do not meet the requirement, and additional commitments should be based on careful analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use preemptible VMs for all production workloads to reduce compute costs.
Why it's wrong here
Preemptible VMs are significantly cheaper but can be terminated at any time, which can impact reliability and performance. Using them for all production workloads would violate the requirement to not sacrifice reliability. They are better suited for fault-tolerant batch jobs, not for critical production services.
- ✓
Implement automatic resource scheduling to shut down non-production VMs during off-hours.
Why this is correct
Automatic resource scheduling stops non-production VMs when they are not needed, such as nights and weekends. This directly reduces Compute Engine costs without affecting production performance or reliability. It is a common and effective cost-optimization practice, especially for development and test environments that do not require 24/7 uptime.
- ✗
Purchase additional committed use discounts for all remaining on-demand instances.
Why it's wrong here
The company has already implemented committed use discounts. Purchasing more without analyzing actual usage could lead to overcommitment and wasted spend if usage decreases. Committed use discounts are beneficial only when usage is predictable and steady; blindly buying more is not a recommended optimization step.
- ✓
Migrate infrequently accessed Cloud Storage data to Nearline or Coldline storage classes.
Why this is correct
Moving data that is accessed less than once a month to Nearline or Coldline storage reduces storage costs because these classes have lower per-GB pricing than Standard. They still provide low-latency access when needed, so performance is not sacrificed for infrequent access patterns. This is a straightforward optimization for media archives and backups.
- ✗
Enable billing export to BigQuery and create cost anomaly detection dashboards.
Why it's wrong here
Billing export and dashboards improve cost visibility and help detect anomalies, but they do not directly reduce spend. They are valuable for monitoring and governance, but the question asks for actions that reduce costs. Visibility alone does not lower the bill unless it leads to further optimization actions.
Go deeper
Related to this question
Learn chapter
Google Cloud Compute Options Overview
Key term
Compute Engine
Compute Engine is Google Cloud's Infrastructure-as-a-Service (IaaS) offering that lets you create and run virtual machines on Google's infrastructure.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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