PMLE Monitoring ML Solutions Practice Question
A company has a model serving predictions on Vertex AI Endpoints and wants to monitor for prediction drift. They enable Vertex AI Model Monitoring but also need to see a confusion matrix over time. How should they set up the confusion matrix monitoring?
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
✓
Upload ground truth data to BigQuery and use Vertex AI Model Monitoring's model quality monitoring
Vertex AI Model Monitoring can compute confusion matrices when ground truth labels are provided in BigQuery. The service compares predictions against actuals and can output confusion matrices over time windows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Monitoring to create a custom dashboard with a confusion matrix chart
Why it's wrong here
Cloud Monitoring does not compute confusion matrices from prediction data directly.
- ✗
Export predictions to Cloud Storage and run a Dataflow job to compute confusion matrices
Why it's wrong here
While possible, Vertex AI Model Monitoring provides this functionality natively, making it the simplest approach.
- ✓
Upload ground truth data to BigQuery and use Vertex AI Model Monitoring's model quality monitoring
Why this is correct
Model quality monitoring in Vertex AI Model Monitoring uses ground truth from BigQuery to generate confusion matrices.
- ✗
Enable Vertex AI Explainable AI and configure it to output confusion matrices
Why it's wrong here
Explainable AI does not compute confusion matrices.
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Variation 1. An organization wants to collect ground truth labels for model quality monitoring and store them in BigQuery. They also want to compute and visualize a confusion matrix over time. Which TWO actions should they take? (Choose 2)
medium- A.Configure Vertex AI Model Monitoring for prediction drift
- B.Use Vertex AI Model Evaluation to run sliced evaluation
- C.Enable request/response logging to Cloud Logging
- ✓ D.Use Vertex AI Model Evaluation to compare predictions with ground truth and generate confusion matrix
- ✓ E.Upload ground truth data to a BigQuery table
Why D: Ground truth labels must be uploaded to BigQuery. Vertex AI Model Evaluation can then be used to compute metrics like confusion matrices.
Variation 2. A data science team wants to monitor model quality by comparing predictions against ground truth labels. They have deployed a model on Vertex AI Endpoints and enable request/response logging to BigQuery. Which THREE actions should they take to set up model quality monitoring? (Choose 3)
hard- ✓ A.Create a scheduled query to compute metrics like accuracy and confusion matrix over time
- ✓ B.Join prediction logs with ground truth labels on a common key (e.g., request ID)
- C.Configure Vertex AI Model Monitoring to detect prediction drift
- D.Use Vertex AI Explainability to compute feature attributions
- ✓ E.Upload ground truth labels to a BigQuery table
Why A: To monitor model quality, the team needs to upload ground truth labels to BigQuery, join with prediction logs, compute metrics (e.g., accuracy, confusion matrix) over time, and optionally create dashboards.
Variation 3. A data scientist has deployed a model with Vertex AI Endpoints and enabled request/response logging to BigQuery. They want to compute a confusion matrix over time to monitor model quality. What should they do?
medium- A.Use Vertex AI Model Monitoring to automatically generate confusion matrices
- B.Use Cloud Monitoring to create a confusion matrix dashboard
- ✓ C.Upload ground truth labels to BigQuery and join with prediction logs, then compute confusion matrix in a scheduled query
- D.Enable Vertex AI Explainability to get confusion matrix
Why C: To compute a confusion matrix, ground truth labels are needed. The team can upload ground truth labels to BigQuery and join with prediction logs to compare predictions vs actuals.
Variation 4. An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?
easy- ✓ A.BigQuery
- B.Firestore
- C.Cloud Spanner
- D.Cloud Storage
Why A: Vertex AI Model Monitoring expects ground truth data to be uploaded to BigQuery tables, which can then be used to compute confusion matrices and other quality metrics over time.
Variation 5. A team wants to collect ground truth labels for their model deployed on Vertex AI Endpoint to perform model quality monitoring. They have a process that generates actual outcomes within 24 hours of prediction. What is the recommended approach for storing these labels?
medium- ✓ A.Upload the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
- B.Use Vertex AI Experiments to log ground truth alongside training runs.
- C.Store the ground truth labels in Cloud Storage as CSV files and reference them in the monitoring config.
- D.Insert ground truth labels directly into the Vertex AI Endpoint's log sink.
Why A: Vertex AI Model Monitoring for model quality requires ground truth data to be uploaded to BigQuery. Labels are stored in a BigQuery table with the prediction timestamp and model ID for comparison.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.