This PCDOE practice question tests your understanding of implementing service monitoring strategies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team wants to monitor the latency of a microservice deployed on GKE. Which Google Cloud tool should they use to collect custom metrics?
Cloud Logging
Why wrong: Cloud Logging is for logs, not custom metrics.
Cloud Trace
Why wrong: Cloud Trace is for distributed tracing, not custom metrics.
Error Reporting
Why wrong: Error Reporting aggregates application errors, not custom metrics.
Cloud Monitoring
Cloud Monitoring collects metrics including custom metrics from GKE.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Monitoring
Cloud Monitoring (formerly Stackdriver Monitoring) is the correct tool because it allows you to collect, visualize, and alert on custom metrics via the Monitoring API or the `custom.googleapis.com` metric namespace. For a microservice on GKE, you can instrument your application with the OpenTelemetry SDK or the Cloud Monitoring client library to push custom latency metrics directly into Cloud Monitoring, where they can be queried and used in dashboards or alerting policies.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Logging
Why it's wrong here
Cloud Logging is for logs, not custom metrics.
Cloud Trace
Why it's wrong here
Cloud Trace is for distributed tracing, not custom metrics.
Error Reporting
Why it's wrong here
Error Reporting aggregates application errors, not custom metrics.
Cloud Monitoring
Why this is correct
Cloud Monitoring collects metrics including custom metrics from GKE.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between logging, tracing, and monitoring, and the trap here is that candidates confuse Cloud Trace (which deals with latency at the trace level) with Cloud Monitoring (which collects and stores custom numeric metrics for alerting and dashboards).
Detailed technical explanation
Under the hood, Cloud Monitoring uses a time-series database that stores metric points with labels, and custom metrics are written using the `projects.timeSeries.create` API endpoint. A real-world scenario is a GKE microservice that emits a custom metric like `custom.googleapis.com/myapp/latency` with a histogram distribution; this allows you to compute percentiles (p50, p99) directly in Monitoring dashboards, which is not possible with Cloud Logging or Trace alone.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Implementing service monitoring strategies — This question tests Implementing service monitoring strategies — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Monitoring — Cloud Monitoring (formerly Stackdriver Monitoring) is the correct tool because it allows you to collect, visualize, and alert on custom metrics via the Monitoring API or the `custom.googleapis.com` metric namespace. For a microservice on GKE, you can instrument your application with the OpenTelemetry SDK or the Cloud Monitoring client library to push custom latency metrics directly into Cloud Monitoring, where they can be queried and used in dashboards or alerting policies.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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