- A
Use Vertex AI Experiments to log metrics and compare across runs
Why wrong: Vertex AI Experiments is designed for tracking training experiments, not for production monitoring of deployed models.
- B
Use Cloud Logging to search logs from each model and create a dashboard
Why wrong: Cloud Logging is event-based and not optimized for aggregated metric dashboards; it would require complex log-based metrics to mimic a monitoring solution.
- C
Use BigQuery to store prediction logs and then visualize in Looker
Why wrong: While possible, this approach adds latency and complexity compared to Cloud Monitoring, which is purpose-built for real-time metrics and alerting.
- D
Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources
Cloud Monitoring supports custom metrics and dashboards that can be filtered by resource labels (e.g., model name, version), providing centralized visibility and drill-down capability.
Quick Answer
The answer is to use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources. This approach is correct because Cloud Monitoring allows each ML model to emit custom metric time series for key performance indicators like prediction accuracy, latency, and error rates, which are then aggregated into a single centralized monitoring dashboard. The dashboard’s filterable resources—by region, model version, or deployment—enable drill-down into individual model versions without relying on log-based or batch analytics, providing real-time observability. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how to implement a centralized monitoring dashboard for multiple ML models using Cloud Monitoring’s custom metrics, a common pattern for production MLOps. A frequent trap is choosing log-based solutions like Cloud Logging or batch tools like BigQuery, which lack real-time metric aggregation and filtering. Memory tip: think “custom metrics + filterable dashboard” as the MLOps trifecta for multi-model observability.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 large enterprise has multiple ML models deployed in production across different regions. They want to implement a centralized monitoring dashboard that tracks key performance indicators such as prediction accuracy, latency, and error rates for all models, with the ability to drill down into individual model versions. Which approach best meets these requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources
Option D is correct because Cloud Monitoring with custom metrics allows each model deployment to report key performance indicators (e.g., prediction accuracy, latency, error rates) as metric time series. These custom metrics can be aggregated into a single unified dashboard, and the dashboard can be configured with filterable resources (e.g., region, model version) to enable drill-down into individual model versions. This approach provides centralized, real-time monitoring without relying on log-based or batch analytics.
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 Vertex AI Experiments to log metrics and compare across runs
Why it's wrong here
Vertex AI Experiments is designed for tracking training experiments, not for production monitoring of deployed models.
- ✗
Use Cloud Logging to search logs from each model and create a dashboard
Why it's wrong here
Cloud Logging is event-based and not optimized for aggregated metric dashboards; it would require complex log-based metrics to mimic a monitoring solution.
- ✗
Use BigQuery to store prediction logs and then visualize in Looker
Why it's wrong here
While possible, this approach adds latency and complexity compared to Cloud Monitoring, which is purpose-built for real-time metrics and alerting.
- ✓
Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources
Why this is correct
Cloud Monitoring supports custom metrics and dashboards that can be filtered by resource labels (e.g., model name, version), providing centralized visibility and drill-down capability.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between logging (Cloud Logging) and monitoring (Cloud Monitoring), where candidates mistakenly think log-based dashboards are sufficient for real-time KPI tracking, ignoring the need for structured, low-latency custom metrics.
Detailed technical explanation
How to think about this question
Custom metrics in Cloud Monitoring are created via the `custom.googleapis.com` metric domain, where each model deployment can emit metrics using the Cloud Monitoring API or client libraries (e.g., `google.cloud.monitoring_v3`). The dashboard can use filterable resource labels (e.g., `model_name`, `region`, `version`) to dynamically slice data, enabling drill-down without separate dashboards. A real-world scenario is a global e-commerce platform monitoring fraud detection models across AWS, GCP, and on-prem, where custom metrics unify observability despite heterogeneous infrastructure.
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 PMLE question test?
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources — Option D is correct because Cloud Monitoring with custom metrics allows each model deployment to report key performance indicators (e.g., prediction accuracy, latency, error rates) as metric time series. These custom metrics can be aggregated into a single unified dashboard, and the dashboard can be configured with filterable resources (e.g., region, model version) to enable drill-down into individual model versions. This approach provides centralized, real-time monitoring without relying on log-based or batch analytics.
What should I do if I get this PMLE 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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 PMLE 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 PMLE exam.
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