- A
Using the Cloud Monitoring API to write metric points.
The monitoring API allows programmatic ingestion of custom metric data points, a standard approach for custom metrics.
- B
Using the OpenTelemetry Collector to export metrics.
OpenTelemetry Collector can export metrics to Cloud Monitoring via the exporter, enabling custom instrumentation.
- C
Using Cloud Console's Metrics Explorer to manually enter data.
Why wrong: Metrics Explorer is for visualizing existing metrics, not for creating new ones; there is no manual data entry functionality.
- D
Creating a log-based metric from Cloud Logging.
Log-based metrics count logs matching a query and automatically appear as custom metrics in Cloud Monitoring.
- E
Using Cloud Functions to emit metrics via Stackdriver Monitoring API.
Why wrong: While Cloud Functions can call the Monitoring API, this is essentially the same as option A; it is not a distinct method and often duplicates the API approach.
PCD Managing application performance monitoring Practice Question
This PCD practice question tests your understanding of managing application performance monitoring. 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.
Which THREE are valid methods to create custom metrics in Cloud Monitoring?
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
Using the Cloud Monitoring API to write metric points.
Option A is correct because the Cloud Monitoring API allows you to write custom metric points directly using the `projects.timeSeries.create` method. This enables you to define your own metric descriptors and send time-series data to Cloud Monitoring, which is a fundamental way to create custom metrics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using the Cloud Monitoring API to write metric points.
Why this is correct
The monitoring API allows programmatic ingestion of custom metric data points, a standard approach for custom metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Using the OpenTelemetry Collector to export metrics.
Why this is correct
OpenTelemetry Collector can export metrics to Cloud Monitoring via the exporter, enabling custom instrumentation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Using Cloud Console's Metrics Explorer to manually enter data.
Why it's wrong here
Metrics Explorer is for visualizing existing metrics, not for creating new ones; there is no manual data entry functionality.
- ✓
Creating a log-based metric from Cloud Logging.
Why this is correct
Log-based metrics count logs matching a query and automatically appear as custom metrics in Cloud Monitoring.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Using Cloud Functions to emit metrics via Stackdriver Monitoring API.
Why it's wrong here
While Cloud Functions can call the Monitoring API, this is essentially the same as option A; it is not a distinct method and often duplicates the API approach.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think Metrics Explorer (option C) can create custom metrics because it allows you to chart data, but it is purely a query interface and cannot ingest new data.
Detailed technical explanation
How to think about this question
Custom metrics in Cloud Monitoring are created by defining a `MetricDescriptor` and then writing time-series data points via the `timeSeries.create` endpoint. The OpenTelemetry Collector (option B) can export metrics to Cloud Monitoring using the OpenTelemetry Protocol (OTLP) or via a Google Cloud exporter, which automatically creates custom metric descriptors. Log-based metrics (option D) are derived from log entries using filters and extractors, and they appear as custom metrics in Cloud Monitoring without needing to write code.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PCD question test?
Managing application performance monitoring — This question tests Managing application performance monitoring — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using the Cloud Monitoring API to write metric points. — Option A is correct because the Cloud Monitoring API allows you to write custom metric points directly using the `projects.timeSeries.create` method. This enables you to define your own metric descriptors and send time-series data to Cloud Monitoring, which is a fundamental way to create custom metrics.
What should I do if I get this PCD question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 25, 2026
This PCD 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 PCD exam.
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