- A
Alert on CPU utilization greater than 80% for the instance running the job
Why wrong: CPU utilization may be high even after the job completes, or low during the job, leading to false negatives.
- B
Create a custom metric that emits 1 when the job starts and 0 when it finishes; alert if the metric is 1 for more than 90 minutes
This directly measures job duration and triggers an alert if it exceeds 90 minutes.
- C
Use a heartbeat metric that reports every 5 minutes; alert if no heartbeat for 90 minutes
Why wrong: No heartbeat for 90 minutes indicates a failure, not that the job is still running.
- D
Set up a log-based metric that counts job completion log entries; alert if the count is zero after 90 minutes
Why wrong: This is indirect; you would need to ensure the log entry is written, and the condition is more complex.
Quick Answer
The correct answer is to create a custom metric that emits 1 when the job starts and 0 when it finishes, then alert if the metric is 1 for more than 90 minutes. This approach directly tracks the job’s running state as a binary signal, allowing you to set a precise threshold for the batch job duration alert without relying on noisy proxies like CPU utilization or log-based latency. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of custom metrics versus built-in system metrics—a common trap is choosing a log-based metric or a VM-level metric like CPU, which can trigger false positives if the job is idle but still processing. The key insight is that a custom metric gives you exact control over the alert condition, aligning with the requirement to catch failures before the 2-hour deadline. Memory tip: think of it as a “start/stop switch”—if the switch stays on past 90 minutes, you know the job is stuck.
PCDOE Implementing service monitoring strategies Practice Question
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.
You are setting up alerting for a batch processing job that runs daily on Compute Engine. The job must complete within 2 hours. Which metric and alert condition should you use to ensure you are notified if the job is still running after 90 minutes?
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 custom metric that emits 1 when the job starts and 0 when it finishes; alert if the metric is 1 for more than 90 minutes
Option B is correct because it directly monitors the job's running state using a custom metric that emits 1 at job start and 0 at completion. By alerting when the metric remains at 1 for more than 90 minutes, you are notified if the job exceeds the 90-minute threshold, ensuring you catch failures before the 2-hour deadline. This approach is precise and avoids false positives from indirect signals like CPU or logs.
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.
- ✗
Alert on CPU utilization greater than 80% for the instance running the job
Why it's wrong here
CPU utilization may be high even after the job completes, or low during the job, leading to false negatives.
- ✓
Create a custom metric that emits 1 when the job starts and 0 when it finishes; alert if the metric is 1 for more than 90 minutes
Why this is correct
This directly measures job duration and triggers an alert if it exceeds 90 minutes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a heartbeat metric that reports every 5 minutes; alert if no heartbeat for 90 minutes
Why it's wrong here
No heartbeat for 90 minutes indicates a failure, not that the job is still running.
- ✗
Set up a log-based metric that counts job completion log entries; alert if the count is zero after 90 minutes
Why it's wrong here
This is indirect; you would need to ensure the log entry is written, and the condition is more complex.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between direct state monitoring (custom metric with start/end signals) and indirect signals (CPU, heartbeats, log counts), where candidates mistakenly choose an indirect metric that seems plausible but fails to accurately capture the specific condition of 'still running after 90 minutes'.
Detailed technical explanation
How to think about this question
Custom metrics in Google Cloud Monitoring are created via the Monitoring API or using the `gcloud` command with a custom metric descriptor. The metric value of 1 or 0 acts as a binary state indicator, and the alert condition uses a 'metric absence' or 'metric value' threshold with a duration of 90 minutes. This pattern is common for monitoring batch jobs because it directly reflects the job's lifecycle, unlike indirect metrics such as CPU or logs, which can be affected by system noise or logging delays. In real-world scenarios, you would instrument the job script to emit the metric at start and end using the `google-cloud-monitoring` client library, ensuring the metric is updated even if the job crashes by using a trap or finally block.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this PCDOE question test?
Implementing service monitoring strategies — This question tests Implementing service monitoring strategies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a custom metric that emits 1 when the job starts and 0 when it finishes; alert if the metric is 1 for more than 90 minutes — Option B is correct because it directly monitors the job's running state using a custom metric that emits 1 at job start and 0 at completion. By alerting when the metric remains at 1 for more than 90 minutes, you are notified if the job exceeds the 90-minute threshold, ensuring you catch failures before the 2-hour deadline. This approach is precise and avoids false positives from indirect signals like CPU or logs.
What should I do if I get this PCDOE 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
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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