PCDOE Optimizing performance and cost Practice Question
A DevOps engineer notices that the monthly compute cost is higher than expected for a Kubernetes Engine cluster. They want to identify which namespaces or workloads are contributing most to the cost. What should they do?
⚠ Common exam trap
Candidates often confuse monitoring metrics (CPU/memory usage) with cost data, or assume node labels and billing reports alone provide namespace-level granularity, missing that GKE Usage Metering is the specific tool designed for this exact use case.
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 GKE Usage Metering and export to BigQuery.
GKE Usage Metering exports detailed per-cluster resource consumption data (CPU, memory, storage, and network) to BigQuery, enabling cost attribution by namespace, label, or workload. This is the only option that directly provides granular cost breakdowns for Kubernetes resources, allowing the engineer to identify which namespaces or workloads drive compute costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Logging for all containers and analyze logs.
Why it's wrong here
Logs do not provide cost information.
- ✗
Use Cloud Monitoring dashboards with Kubernetes metrics.
Why it's wrong here
Cloud Monitoring shows resource usage, not cost allocation.
- ✗
Add labels to nodes and use Cost Table reports.
Why it's wrong here
Labels on nodes apply to the whole node, not per workload.
- ✓
Enable GKE Usage Metering and export to BigQuery.
Why this is correct
GKE Usage Metering provides cost attribution by namespace and workload.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PCDOE 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 PCDOE exam.