- A
Configure Vertex AI Model Monitoring to compare online prediction inputs against training data statistics.
This detects feature drift, which is a common monitoring need.
- B
Collect ground truth labels for all predictions to measure accuracy drift.
Why wrong: Ground truth is not required for drift detection; drift is based on distribution changes.
- C
Set up a separate Cloud Monitoring alerting policy to watch for prediction errors.
Why wrong: Vertex AI Model Monitoring already integrates with Cloud Monitoring for alerts.
- D
Enable automatic model retraining in Vertex AI Model Monitoring when drift is detected.
Why wrong: Model Monitoring only sends alerts; retraining must be triggered separately.
- E
Enable prediction drift monitoring to detect changes in model output distribution.
This helps identify when the model's predictions shift over time.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. 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 company is deploying a machine learning model for fraud detection. The model is trained using TensorFlow and will be served on Vertex AI Prediction. The team wants to implement model monitoring to detect prediction drift. Which TWO actions should they take? (Choose 2)
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 Vertex AI Model Monitoring to compare online prediction inputs against training data statistics.
Option A is correct because Vertex AI Model Monitoring can be configured to compare online prediction inputs against training data statistics to detect skew, which is a form of drift. This is a standard capability of Vertex AI Model Monitoring, where you specify a baseline dataset (typically training data) and the service automatically computes statistics on incoming prediction requests to identify distribution shifts.
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.
- ✓
Configure Vertex AI Model Monitoring to compare online prediction inputs against training data statistics.
Why this is correct
This detects feature drift, which is a common monitoring need.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Collect ground truth labels for all predictions to measure accuracy drift.
Why it's wrong here
Ground truth is not required for drift detection; drift is based on distribution changes.
- ✗
Set up a separate Cloud Monitoring alerting policy to watch for prediction errors.
Why it's wrong here
Vertex AI Model Monitoring already integrates with Cloud Monitoring for alerts.
- ✗
Enable automatic model retraining in Vertex AI Model Monitoring when drift is detected.
Why it's wrong here
Model Monitoring only sends alerts; retraining must be triggered separately.
- ✓
Enable prediction drift monitoring to detect changes in model output distribution.
Why this is correct
This helps identify when the model's predictions shift over time.
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 monitoring for drift (which focuses on input/output distributions) versus monitoring for model accuracy (which requires ground truth labels), and candidates mistakenly think collecting ground truth is a prerequisite for drift detection.
Detailed technical explanation
How to think about this question
Prediction drift monitoring in Vertex AI works by computing the distribution of model outputs (e.g., softmax scores or class probabilities) over a sliding window and comparing them to a baseline distribution using statistical tests like the Kolmogorov-Smirnov test or Jensen-Shannon divergence. For input drift, the service compares feature distributions of incoming requests against training data statistics using methods like the L-infinity distance for categorical features or the Mahalanobis distance for numerical features. The monitoring job runs asynchronously, typically every hour, and can be configured with custom alert thresholds.
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.
- →
Operationalizing machine learning models — study guide chapter
Learn the concepts, then practise the questions
- →
Operationalizing machine learning models practice questions
Targeted practice on this topic area only
- →
All PDE questions
499 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing data processing systems practice questions
Practise PDE questions linked to Designing data processing systems.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE 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 PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure Vertex AI Model Monitoring to compare online prediction inputs against training data statistics. — Option A is correct because Vertex AI Model Monitoring can be configured to compare online prediction inputs against training data statistics to detect skew, which is a form of drift. This is a standard capability of Vertex AI Model Monitoring, where you specify a baseline dataset (typically training data) and the service automatically computes statistics on incoming prediction requests to identify distribution shifts.
What should I do if I get this PDE 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: Jun 30, 2026
This PDE 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 PDE 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.