- A
Configure feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence
Vertex AI Feature Store allows setting drift thresholds per feature view, using methods like JS divergence.
- B
Use BigQuery scheduled queries to compare distributions and send alerts
Why wrong: This is a manual approach; Vertex AI Feature Store provides built-in monitoring.
- C
Set up a Cloud Composer DAG to compute drift and publish to Cloud Monitoring
Why wrong: While possible, it's not the native approach; Feature Store monitoring is simpler.
- D
Enable model monitoring on the Vertex AI endpoint to detect drift
Why wrong: Model monitoring detects drift on prediction inputs, not features in the feature store.
Question 496 of 1,000
Collaborating Within and Across Teams to Manage Data and Models →mediumMultiple ChoiceObjective-mapped
PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and 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.
You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?
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 feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence
Feature monitoring in Vertex AI Feature Store uses monitoring_config with drift detection via statistical tests like Jensen-Shannon divergence. The correct approach is to set the drift threshold in the feature view's monitoring configuration.
Key principle: Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
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 feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence
Why this is correct
Vertex AI Feature Store allows setting drift thresholds per feature view, using methods like JS divergence.
Related concept
Authentication checks who the user is.
- ✗
Use BigQuery scheduled queries to compare distributions and send alerts
Why it's wrong here
This is a manual approach; Vertex AI Feature Store provides built-in monitoring.
- ✗
Set up a Cloud Composer DAG to compute drift and publish to Cloud Monitoring
Why it's wrong here
While possible, it's not the native approach; Feature Store monitoring is simpler.
- ✗
Enable model monitoring on the Vertex AI endpoint to detect drift
Why it's wrong here
Model monitoring detects drift on prediction inputs, not features in the feature store.
Common exam traps
Common exam trap: authentication is not authorization
Logging in proves the user can authenticate. It does not automatically mean the user is allowed to enter privileged or configuration mode. Watch for AAA authorization, privilege level and command authorization details.
Detailed technical explanation
How to think about this question
This kind of question is testing the difference between identity and permission. A user may successfully log in to a router because authentication is working, but still fail to enter configuration mode because authorization is missing, misconfigured or mapped to a lower privilege level.
KKey Concepts to Remember
- Authentication checks who the user is.
- Authorization controls what the user is allowed to do after login.
- Privilege levels affect access to EXEC and configuration commands.
- AAA, TACACS+ and RADIUS can separate login success from command access.
TExam Day Tips
- Do not assume successful login means full administrative access.
- Look for words such as cannot enter configuration mode, privilege level, authorization or command access.
- Separate login problems from permission problems before choosing the answer.
Key takeaway
Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
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.
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related PMLE questions on access control and AAA configuration.
- →
Collaborating Within and Across Teams to Manage Data and Models — study guide chapter
Learn the concepts, then practise the questions
- →
Collaborating Within and Across Teams to Manage Data and Models 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?
Collaborating Within and Across Teams to Manage Data and Models — This question tests Collaborating Within and Across Teams to Manage Data and Models — Authentication checks who the user is..
What is the correct answer to this question?
The correct answer is: Configure feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence — Feature monitoring in Vertex AI Feature Store uses monitoring_config with drift detection via statistical tests like Jensen-Shannon divergence. The correct approach is to set the drift threshold in the feature view's monitoring configuration.
What should I do if I get this PMLE question wrong?
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related PMLE questions on access control and AAA configuration.
What is the key concept behind this question?
Authentication checks who the user is.
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 →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
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.