- A
Export ground truth labels to Cloud Storage
Why wrong: Ground truth is needed for quality monitoring, not for skew/drift detection.
- B
Enable request/response logging on the Vertex AI Endpoint
Logging captures the serving data needed for monitoring.
- C
Enable Vertex AI Pipelines to run scheduled monitoring
Why wrong: Pipelines are not required for monitoring; ModelMonitoringJob runs independently.
- D
Create a ModelMonitoringJob with a monitoring configuration
The monitoring job defines what to monitor and how.
- E
Deploy the model with an explanation spec
Why wrong: Explanation spec is for explainability, not for basic drift monitoring.
Question 914 of 1,000
PMLE Monitoring ML Solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
An MLOps engineer is setting up monitoring for a deployed model on Vertex AI Endpoints. Which TWO actions are required to enable Vertex AI Model Monitoring for feature skew and drift? (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
✓
Enable request/response logging on the Vertex AI Endpoint
To enable model monitoring, you must enable request/response logging on the endpoint (to capture serving data) and create a monitoring job with the desired 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.
- ✗
Export ground truth labels to Cloud Storage
Why it's wrong here
Ground truth is needed for quality monitoring, not for skew/drift detection.
- ✓
Enable request/response logging on the Vertex AI Endpoint
Why this is correct
Logging captures the serving data needed for monitoring.
Related concept
Authentication checks who the user is.
- ✗
Enable Vertex AI Pipelines to run scheduled monitoring
Why it's wrong here
Pipelines are not required for monitoring; ModelMonitoringJob runs independently.
- ✓
Create a ModelMonitoringJob with a monitoring configuration
Why this is correct
The monitoring job defines what to monitor and how.
Related concept
Authentication checks who the user is.
- ✗
Deploy the model with an explanation spec
Why it's wrong here
Explanation spec is for explainability, not for basic drift monitoring.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML Solutions — This question tests Monitoring ML Solutions — Authentication checks who the user is..
What is the correct answer to this question?
The correct answer is: Enable request/response logging on the Vertex AI Endpoint — To enable model monitoring, you must enable request/response logging on the endpoint (to capture serving data) and create a monitoring job with the desired 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.
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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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