- A
Create a Cloud Monitoring custom metric and write values via the application's exception handler
Why wrong: Writing from the app requires code changes — log-based metrics extract the count directly from existing log entries without modifying the application.
- B
Create a log-based metric in Cloud Logging with a filter matching 'PaymentFailure', then alert on it in Cloud Monitoring
Log-based metrics automatically count (or extract values from) log entries matching a filter. The resulting time-series metric is immediately usable in Cloud Monitoring alerting policies.
- C
Export logs to BigQuery and run a scheduled query counting PaymentFailure entries
Why wrong: BigQuery queries run on a schedule with latency — log-based metrics provide near-real-time counting without pipeline complexity.
- D
Enable Cloud Trace and look for PaymentFailure in trace annotations
Why wrong: Cloud Trace tracks request latency — it's not designed for log pattern counting or alerting on error frequencies.
Quick Answer
The correct approach is to create a log-based metric in Cloud Logging with a filter matching 'PaymentFailure', then alert on it in Cloud Monitoring. This works because log-based metrics allow you to define a filter such as `textPayload:"PaymentFailure"` that counts matching log entries in real time, and Cloud Monitoring can directly create an alerting policy on that metric with a threshold of 10 occurrences per minute, leveraging the native integration between the two services without custom code or external exports. On the Google Associate Cloud Engineer exam, this scenario tests your understanding that Cloud Monitoring lacks native metrics for arbitrary log patterns, so you must bridge the gap using log-based metrics—a common trap is choosing to export logs to BigQuery or Pub/Sub unnecessarily. Remember the memory tip: “Log first, then alert”—always create the log-based metric in Cloud Logging before setting the threshold in Cloud Monitoring.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
An operations team wants to count how many times the string 'PaymentFailure' appears in application logs per minute and alert when it exceeds 10 occurrences. Cloud Monitoring doesn't have a native metric for this log pattern. What is the correct approach?
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
Create a log-based metric in Cloud Logging with a filter matching 'PaymentFailure', then alert on it in Cloud Monitoring
Option B is correct because Cloud Logging log-based metrics allow you to define a filter (e.g., `textPayload:"PaymentFailure"`) that counts matching log entries in real time, and Cloud Monitoring can directly create an alerting policy on that metric with a threshold of 10 occurrences per minute. This approach avoids custom code, external exports, or additional services, and it leverages the native integration between Cloud Logging and Cloud Monitoring.
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.
- ✗
Create a Cloud Monitoring custom metric and write values via the application's exception handler
Why it's wrong here
Writing from the app requires code changes — log-based metrics extract the count directly from existing log entries without modifying the application.
- ✓
Create a log-based metric in Cloud Logging with a filter matching 'PaymentFailure', then alert on it in Cloud Monitoring
Why this is correct
Log-based metrics automatically count (or extract values from) log entries matching a filter. The resulting time-series metric is immediately usable in Cloud Monitoring alerting policies.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Export logs to BigQuery and run a scheduled query counting PaymentFailure entries
Why it's wrong here
BigQuery queries run on a schedule with latency — log-based metrics provide near-real-time counting without pipeline complexity.
- ✗
Enable Cloud Trace and look for PaymentFailure in trace annotations
Why it's wrong here
Cloud Trace tracks request latency — it's not designed for log pattern counting or alerting on error frequencies.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between native log-based metrics (which require no code or external services) and custom metrics or export-based solutions, leading candidates to overcomplicate the answer by choosing BigQuery or custom code.
Detailed technical explanation
How to think about this question
Log-based metrics in Cloud Logging are evaluated at ingestion time, meaning the count is updated as logs arrive, and the metric is available in Cloud Monitoring with a typical latency of under 60 seconds. The filter uses the Cloud Logging query language, which supports exact string matching with `textPayload:"PaymentFailure"` or regex with `regex`. Alerting policies can then use a rolling window (e.g., 1 minute) and a threshold condition (e.g., >10) to trigger notifications via Pub/Sub, email, or webhooks.
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.
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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: Create a log-based metric in Cloud Logging with a filter matching 'PaymentFailure', then alert on it in Cloud Monitoring — Option B is correct because Cloud Logging log-based metrics allow you to define a filter (e.g., `textPayload:"PaymentFailure"`) that counts matching log entries in real time, and Cloud Monitoring can directly create an alerting policy on that metric with a threshold of 10 occurrences per minute. This approach avoids custom code, external exports, or additional services, and it leverages the native integration between Cloud Logging and Cloud Monitoring.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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