- A
Use Cloud Monitoring's Metrics Explorer to create a metric from logs using a filter.
Why wrong: Metrics Explorer visualizes existing metrics but cannot create new metrics from logs.
- B
Install the Ops Agent on GKE nodes to collect application metrics directly.
Why wrong: The Ops Agent collects system and application metrics from VMs, not from logs; it cannot parse log content to count events.
- C
Configure a logs-based metric in Cloud Logging that filters for the condition and counts.
Logs-based metrics are designed for this purpose – they count log entries that match a filter and expose them as custom metrics in Cloud Monitoring.
- D
Export logs to BigQuery and then create a custom metric from the exported data.
Why wrong: This approach is indirect, costly, and introduces latency; logs-based metrics are simpler and real-time.
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 designing a monitoring strategy for a microservices application running on Google Kubernetes Engine (GKE). You need to create a custom metric that counts the number of failed login attempts from the application logs. The logs are in JSON format and contain a field 'status' with value 'FAILED'. Which approach should you use?
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
Configure a logs-based metric in Cloud Logging that filters for the condition and counts.
Option C is correct because a logs-based metric in Cloud Logging directly counts occurrences of a specific log entry pattern (e.g., 'status' = 'FAILED') without requiring additional agents or data exports. This approach is purpose-built for deriving metrics from log data and integrates seamlessly with Cloud Monitoring for alerting and dashboards.
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.
- ✗
Use Cloud Monitoring's Metrics Explorer to create a metric from logs using a filter.
Why it's wrong here
Metrics Explorer visualizes existing metrics but cannot create new metrics from logs.
- ✗
Install the Ops Agent on GKE nodes to collect application metrics directly.
Why it's wrong here
The Ops Agent collects system and application metrics from VMs, not from logs; it cannot parse log content to count events.
- ✓
Configure a logs-based metric in Cloud Logging that filters for the condition and counts.
Why this is correct
Logs-based metrics are designed for this purpose – they count log entries that match a filter and expose them as custom metrics in Cloud Monitoring.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Export logs to BigQuery and then create a custom metric from the exported data.
Why it's wrong here
This approach is indirect, costly, and introduces latency; logs-based metrics are simpler and real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Metrics Explorer can create metrics from logs, when in fact it only queries and charts existing metrics, while logs-based metrics are the correct service for deriving custom metrics from log data.
Detailed technical explanation
How to think about this question
Logs-based metrics in Cloud Logging use a filter (e.g., `jsonPayload.status="FAILED"`) and a counter configuration to increment a metric each time a matching log entry is ingested. The metric is available in Cloud Monitoring within seconds, and you can set up alerting policies or use it in dashboards. A subtle behavior: logs-based metrics are sampled at the log ingestion layer, so they count every occurrence even if the log volume is high, but they do not support distribution or histogram types—only counter or distribution (for numeric values extracted from logs).
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 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: Configure a logs-based metric in Cloud Logging that filters for the condition and counts. — Option C is correct because a logs-based metric in Cloud Logging directly counts occurrences of a specific log entry pattern (e.g., 'status' = 'FAILED') without requiring additional agents or data exports. This approach is purpose-built for deriving metrics from log data and integrates seamlessly with Cloud Monitoring for alerting and dashboards.
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.
About these practice questions
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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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