- A
Enable the Custom Metrics feature flag in the GCP Console under Cloud Monitoring settings
Why wrong: There is no 'Custom Metrics feature flag' — custom metrics work out of the box once data is written to the Cloud Monitoring API.
- B
Instrument the application to write the metric to the Cloud Monitoring API using a client library or OpenTelemetry
The application must emit the custom metric to Cloud Monitoring via the Monitoring API, client library (e.g., google-cloud-monitoring), or OpenTelemetry SDK. Once flowing, it appears in Metrics Explorer.
- C
Create a log-based metric that extracts the orders value from application logs
Why wrong: Log-based metrics extract numeric values from log entries — valid if the app logs orders-per-minute, but not required if the app can directly emit metric data to the Monitoring API.
- D
Custom metrics require BigQuery — store values in BigQuery and link it to Cloud Monitoring
Why wrong: Cloud Monitoring custom metrics are written directly to the Monitoring API — BigQuery is not required or used as a backend for real-time monitoring metrics.
Quick Answer
The answer is that you must first instrument the application to write the custom metric to the Cloud Monitoring API using a client library or OpenTelemetry. This is required because Cloud Monitoring does not automatically discover or ingest custom metrics emitted from application code; the metric data must be explicitly pushed into the API to create the metric descriptor. Without this instrumentation step, the custom business metric—such as orders per minute—simply does not exist in the system, making it impossible to display on a dashboard or configure an alert for thresholds like dropping below 50. On the Google Associate Cloud Engineer exam, this question tests your understanding that custom metrics are not passive; they require active code changes, unlike standard infrastructure metrics. A common trap is assuming Cloud Monitoring can scrape or detect arbitrary application metrics automatically. Remember the key mnemonic: “Instrument first, alert later”—you cannot alert on a metric that hasn’t been written.
Google ACE Practice Question: Ensuring successful operation of a cloud solution
This ACE practice question tests your understanding of ensuring successful operation of a cloud solution. 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's application emits a custom business metric (orders per minute) via its code. They want to display this metric on a Cloud Monitoring dashboard and alert when it drops below 50 orders per minute. What must be done first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Instrument the application to write the metric to the Cloud Monitoring API using a client library or OpenTelemetry
Option B is correct because Cloud Monitoring requires metrics to be explicitly ingested via its API or through OpenTelemetry. Custom metrics are not automatically available; the application must be instrumented to write the metric data (e.g., using the `cloud.google.com/go/monitoring` client library or OpenTelemetry exporter) to the Cloud Monitoring API. Without this step, the metric does not exist in Cloud Monitoring for dashboards or alerts.
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.
- ✗
Enable the Custom Metrics feature flag in the GCP Console under Cloud Monitoring settings
Why it's wrong here
There is no 'Custom Metrics feature flag' — custom metrics work out of the box once data is written to the Cloud Monitoring API.
- ✓
Instrument the application to write the metric to the Cloud Monitoring API using a client library or OpenTelemetry
Why this is correct
The application must emit the custom metric to Cloud Monitoring via the Monitoring API, client library (e.g., google-cloud-monitoring), or OpenTelemetry SDK. Once flowing, it appears in Metrics Explorer.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a log-based metric that extracts the orders value from application logs
Why it's wrong here
Log-based metrics extract numeric values from log entries — valid if the app logs orders-per-minute, but not required if the app can directly emit metric data to the Monitoring API.
- ✗
Custom metrics require BigQuery — store values in BigQuery and link it to Cloud Monitoring
Why it's wrong here
Cloud Monitoring custom metrics are written directly to the Monitoring API — BigQuery is not required or used as a backend for real-time monitoring metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that custom metrics require a feature flag or a separate storage service like BigQuery, when in reality the only prerequisite is instrumenting the application to send data to the Cloud Monitoring API.
Detailed technical explanation
How to think about this question
When using OpenTelemetry, the application exports metrics via the OTLP protocol to the Cloud Monitoring API, which automatically creates a custom metric descriptor in the `custom.googleapis.com/` namespace. The metric must be written as a time series with a `MetricKind` (e.g., GAUGE) and a `ValueType` (e.g., DOUBLE) before it can be used in dashboards or alerting policies. A common subtlety is that the metric descriptor is auto-created on the first write, but if the write fails or is delayed, the metric will not appear in the Metrics Explorer.
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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Ensuring successful operation of a cloud solution — study guide chapter
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FAQ
Questions learners often ask
What does this ACE question test?
Ensuring successful operation of a cloud solution — This question tests Ensuring successful operation of a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Instrument the application to write the metric to the Cloud Monitoring API using a client library or OpenTelemetry — Option B is correct because Cloud Monitoring requires metrics to be explicitly ingested via its API or through OpenTelemetry. Custom metrics are not automatically available; the application must be instrumented to write the metric data (e.g., using the `cloud.google.com/go/monitoring` client library or OpenTelemetry exporter) to the Cloud Monitoring API. Without this step, the metric does not exist in Cloud Monitoring for dashboards or alerts.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 30, 2026
This ACE 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 ACE exam.
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