- A
Cloud Logging
Why wrong: Logging is passive; not for skew detection.
- B
Vertex AI Model Monitoring
For skew detection.
- C
Cloud Functions
Why wrong: Could be used to trigger pipeline but not for skew detection.
- D
Vertex AI Pipelines
For orchestrating retraining.
- E
Cloud Monitoring
Why wrong: Monitoring is for metrics, not dedicated skew detection.
Question 436 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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.
An ML engineer is building a continuous training pipeline that retrains a model when new data arrives. The pipeline should also detect skew between training and serving data. Which TWO Google Cloud services should they use? (Choose two.)
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
✓
Vertex AI Model Monitoring
Vertex AI Model Monitoring (B) is correct because it is purpose-built to detect skew between training and serving data by continuously comparing feature distributions and alerting on statistically significant drift. Vertex AI Pipelines (D) is correct because it provides a serverless, scalable orchestration service for building continuous training pipelines that automatically retrain models when new data arrives, integrating with Cloud Build and other services.
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.
- ✗
Cloud Logging
Why it's wrong here
Logging is passive; not for skew detection.
- ✓
Vertex AI Model Monitoring
Why this is correct
For skew detection.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Functions
Why it's wrong here
Could be used to trigger pipeline but not for skew detection.
- ✓
Vertex AI Pipelines
Why this is correct
For orchestrating retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Monitoring
Why it's wrong here
Monitoring is for metrics, not dedicated skew detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google PMLE exam often tests the distinction between monitoring for infrastructure health (Cloud Monitoring) versus monitoring for ML-specific data skew (Vertex AI Model Monitoring), leading candidates to confuse general observability with ML-specific drift detection.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses techniques like Jensen-Shannon divergence or L-infinity distance to compare training and serving feature distributions, and it can be configured to monitor for skew on a per-feature basis with customizable alert thresholds. Vertex AI Pipelines leverages Kubeflow Pipelines under the hood, allowing you to define directed acyclic graphs (DAGs) of components that can include data validation, model training, evaluation, and deployment steps, with automatic retries and artifact tracking via ML Metadata.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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Automating and Orchestrating ML Pipelines — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Model Monitoring — Vertex AI Model Monitoring (B) is correct because it is purpose-built to detect skew between training and serving data by continuously comparing feature distributions and alerting on statistically significant drift. Vertex AI Pipelines (D) is correct because it provides a serverless, scalable orchestration service for building continuous training pipelines that automatically retrain models when new data arrives, integrating with Cloud Build and other services.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 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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