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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #285

You are training and deploying updated versions of a regression model with tabular data by using Vertex AI Pipelines, Vertex AI Training, Vertex AI Experiments, and Vertex AI Endpoints. The model is…

The correct answer is A. Use Vertex Al Model Monitoring. Enable prediction drift monitoring on the endpoint, and specify a. To receive email alerts when feature data distribution changes significantly for a deployed Vertex AI model, configure Vertex AI Model Monitoring and enable prediction drift monitoring on the endpoint.

Submitted by noor.lb· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

You are training and deploying updated versions of a regression model with tabular data by using Vertex AI Pipelines, Vertex AI Training, Vertex AI Experiments, and Vertex AI Endpoints. The model is deployed in a Vertex AI endpoint, and your users call the model by using the Vertex AI endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model. What should you do?

Options

  • AUse Vertex Al Model Monitoring. Enable prediction drift monitoring on the endpoint, and specify a
  • BIn Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure
  • CIn Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure
  • DExport the container logs of the endpoint to BigQuery. Create a Cloud Function to run a SQL

How the community answered

(63 responses)
  • A
    95% (60)
  • B
    2% (1)
  • C
    3% (2)

Why each option

To receive email alerts when feature data distribution changes significantly for a deployed Vertex AI model, configure Vertex AI Model Monitoring and enable prediction drift monitoring on the endpoint.

AUse Vertex Al Model Monitoring. Enable prediction drift monitoring on the endpoint, and specify aCorrect

Vertex AI Model Monitoring is specifically designed to detect data and prediction drift in deployed models by continuously analyzing incoming prediction requests. Enabling prediction drift monitoring on the endpoint allows the system to automatically detect significant changes in feature data distribution and trigger configured alerts, such as emails.

BIn Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure

While Cloud Logging can be used for general alerts, detecting 'significant changes in feature data distribution' requires complex ML-specific logic that is not inherently provided by basic logs-based alerts.

CIn Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure

Cloud Monitoring can create metrics and alerts, but detecting nuanced 'feature data distribution changes' for ML models is a specialized task better handled by dedicated model monitoring services.

DExport the container logs of the endpoint to BigQuery. Create a Cloud Function to run a SQL

Exporting logs to BigQuery and using a Cloud Function for SQL analysis would require substantial custom development to replicate the advanced drift detection capabilities offered by Vertex AI Model Monitoring.

Concept tested: Vertex AI Model Monitoring for prediction drift

Source: https://cloud.google.com/vertex-ai/docs/model-monitoring/overview

Topics

#Model Monitoring#Data Drift#Vertex AI Endpoints#MLOps

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