Question 205 of 506
Collaborating to manage data and modelsmediumMultiple SelectObjective-mapped

PMLE Collaborating to manage data and models Practice Question

This PMLE practice question tests your understanding of collaborating to manage data and models. 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.

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

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.

Question 1mediummulti select
Full question →

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

Monitor model performance and data drift continuously.

Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

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.

  • Manually tune hyperparameters for each retraining run.

    Why it's wrong here

    Incorrect: automated tuning is more efficient.

  • Monitor model performance and data drift continuously.

    Why this is correct

    Correct: monitoring helps detect degradation.

    Clue confirmation

    The clue word "best" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Use a central model registry for model governance.

    Why this is correct

    Correct: registry provides control and auditability.

    Clue confirmation

    The clue word "best" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Version all model artifacts and training datasets.

    Why this is correct

    Correct: versioning ensures reproducibility.

    Clue confirmation

    The clue word "best" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Store all raw training data indefinitely for auditability.

    Why it's wrong here

    Incorrect: data retention should follow policy, not indefinite.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the misconception that manual hyperparameter tuning is acceptable for production, when in fact automation (e.g., Vertex AI Vizier) is the recommended practice to ensure reproducibility and efficiency.

Detailed technical explanation

How to think about this question

Under the hood, Vertex AI Model Monitoring uses statistical tests like the Kolmogorov-Smirnov test for numerical features and chi-squared tests for categorical features to detect drift. It compares the serving distribution against a baseline distribution computed from training data, and you can set alert thresholds (e.g., p-value < 0.05). In a real-world scenario, a model trained on pre-pandemic data might silently degrade as user behavior shifts, and drift monitoring catches this before the model's predictions become unreliable.

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.

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.

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?

Collaborating to manage data and models — This question tests Collaborating to manage data and models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Monitor model performance and data drift continuously. — Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

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

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 →

How Courseiva writes practice questions · Editorial policy

Keep practising

More PMLE practice questions

Last reviewed: Jun 30, 2026

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.

Loading comments…

Sign in to join the discussion.

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.