PMLE Monitoring ML Solutions Practice Question
Your team has deployed a tabular model to a Vertex AI Endpoint and enabled Vertex AI Model Monitoring with training-serving skew detection. You configured the monitoring job to run hourly and store statistics in a Cloud Storage bucket. After the first run, you notice that the job did not produce any drift metrics. You want to determine the cause of the missing metrics. What should you do first?
⚠ Common exam trap
The trap here is assuming that missing metrics are caused by insufficient sampling or permissions, while the most common cause is a missing or misconfigured reference dataset.
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
✓
Check that the endpoint's deployed model has a reference dataset specified and that the monitoring job's input schema matches the endpoint's prediction schema.
Vertex AI Model Monitoring requires a reference dataset and a matching schema to compute training-serving skew. Without these, the job cannot generate metrics. Checking the configuration of the reference dataset and schema alignment is the logical first step to resolve missing metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the monitoring job's sampling rate to 1.0 to ensure that all prediction requests are analyzed.
Why it's wrong here
Sampling rate affects the volume of data analyzed, but even a low rate would produce some metrics if the job is configured correctly. A sampling rate of 1.0 might increase cost without addressing the root cause of missing metrics, which is more likely a configuration issue such as a missing reference dataset.
- ✗
Re-deploy the model to the endpoint with a larger machine type to handle the monitoring workload.
Why it's wrong here
Monitoring runs as a separate job and does not consume resources on the deployed model's serving container. Increasing the machine type of the endpoint would not affect the monitoring job's ability to generate metrics and would unnecessarily increase cost. The issue is unrelated to serving capacity.
- ✗
Verify that the Cloud Storage bucket has the correct IAM permissions for the Vertex AI service account to write objects.
Why it's wrong here
While IAM permissions are important for storing output, lack of write access would typically result in an error in the job logs rather than a silent absence of metrics. The more fundamental requirement is that the monitoring job has a valid reference dataset and schema to compute skew, so checking permissions is secondary.
- ✓
Check that the endpoint's deployed model has a reference dataset specified and that the monitoring job's input schema matches the endpoint's prediction schema.
Why this is correct
Monitoring requires a reference dataset (the baseline) and a matching schema to compute skew. If the reference dataset is missing or the schema does not align with the endpoint's inputs, the job cannot compute metrics and may silently produce no output. Verifying these configurations is the correct first step to diagnose the issue.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.