- A
Export metrics to Cloud Monitoring and set up alerting on mean values.
Why wrong: Mean values are insufficient for drift detection; full distribution comparison is needed.
- B
Use Vertex AI Model Monitoring with skew detection enabled.
Model Monitoring provides built-in skew and drift detection by comparing distributions.
- C
Use Cloud DLP to inspect the datasets and generate summary statistics.
Why wrong: Cloud DLP is for data classification and de-identification, not distribution comparison.
- D
Compute histograms of features in BigQuery ML and compare them manually.
Why wrong: Manual comparison is not scalable; Model Monitoring automates this.
Question 895 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 team is building a continuous training pipeline that retrains a model when new data arrives. They want to detect data drift between the training dataset and the serving data. Which approach should they integrate into the pipeline to compare the distributions of the two datasets?
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 Vertex AI Model Monitoring with skew detection enabled.
Vertex AI Model Monitoring with skew detection enabled is the correct approach because it is specifically designed to detect data drift between training and serving datasets in a continuous training pipeline. It automatically computes distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) for each feature and compares the training data distribution against the serving data distribution, triggering alerts when drift exceeds a configured threshold. This integrates natively with Vertex AI Pipelines, enabling automated retraining workflows.
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.
- ✗
Export metrics to Cloud Monitoring and set up alerting on mean values.
Why it's wrong here
Mean values are insufficient for drift detection; full distribution comparison is needed.
- ✓
Use Vertex AI Model Monitoring with skew detection enabled.
Why this is correct
Model Monitoring provides built-in skew and drift detection by comparing distributions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud DLP to inspect the datasets and generate summary statistics.
Why it's wrong here
Cloud DLP is for data classification and de-identification, not distribution comparison.
- ✗
Compute histograms of features in BigQuery ML and compare them manually.
Why it's wrong here
Manual comparison is not scalable; Model Monitoring automates this.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between monitoring for data quality (e.g., missing values, outliers) versus monitoring for distribution drift, and candidates mistakenly choose Cloud Monitoring or manual histogram comparison because they think any metric or visualization can detect drift, but only dedicated drift detection services like Vertex AI Model Monitoring provide the necessary statistical tests and automated alerting.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses distribution distance metrics such as Jensen-Shannon divergence for categorical features and L-infinity distance for numerical features, comparing the training feature distribution (reference) against the serving distribution (target). It also supports slicing by feature values to detect localized drift, and can be configured with alerting thresholds (e.g., a divergence score > 0.2) that trigger pipeline retraining. In a real-world scenario, if a model trained on e-commerce data from 2023 sees a sudden shift in user demographics in 2024, skew detection would catch the drift in features like 'age' or 'location' before model accuracy degrades.
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
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.
- →
Automating and Orchestrating ML Pipelines — study guide chapter
Learn the concepts, then practise the questions
- →
Automating and Orchestrating ML Pipelines practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use Vertex AI Model Monitoring with skew detection enabled. — Vertex AI Model Monitoring with skew detection enabled is the correct approach because it is specifically designed to detect data drift between training and serving datasets in a continuous training pipeline. It automatically computes distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) for each feature and compares the training data distribution against the serving data distribution, triggering alerts when drift exceeds a configured threshold. This integrates natively with Vertex AI Pipelines, enabling automated retraining workflows.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.