PCDOE Implementing service monitoring strategies Practice Question
Your team wants to create a dashboard that shows request latency broken down by API version. Which approach is most efficient?
⚠ Common exam trap
Google Cloud often tests the misconception that exporting logs to BigQuery or using Cloud Trace is always better for analysis, but here the question specifically asks for the most efficient approach to display a pre-existing metric broken down by a label, which Metrics Explorer does directly without extra steps or cost.
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
✓
Use Cloud Monitoring Metrics Explorer to query the latency metric, group by API version, and save the chart as a dashboard
Cloud Monitoring Metrics Explorer allows you to directly query the latency metric (e.g., `request_latencies`) and group by the `version` label, then save the resulting chart as a dashboard widget. This is the most efficient approach because it requires no data export, no custom code, and no additional services — the metric is already available in Cloud Monitoring if your API is instrumented with the appropriate label.
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 Cloud Monitoring Metrics Explorer to query the latency metric, group by API version, and save the chart as a dashboard
Why this is correct
Metrics Explorer directly supports grouping and creating dashboards.
- ✗
Write a custom application to output metrics to a file and send to Cloud Monitoring
Why it's wrong here
This is inefficient compared to using existing metrics.
- ✗
Export Cloud Logging logs to BigQuery and create a dashboard in Data Studio
Why it's wrong here
Exporting Cloud Logging to BigQuery and visualising in Data Studio introduces unnecessary latency and cost for real-time dashboarding, as BigQuery is optimised for analytical queries on large historical datasets rather than low-latency streaming. This approach is tempting because BigQuery and Data Studio are excellent for deep ad-hoc analysis of aggregated logs over long time windows. It would be the correct choice if the team needed to run complex SQL queries across months of latency data to identify trends, rather than displaying current API version performance.
- ✗
Enable Cloud Trace and create a dashboard based on trace data
Why it's wrong here
Cloud Trace is for distributed tracing, not direct dashboard creation.
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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.