How to Configure Vertex AI Model Monitoring for Multiple Models with Traffic Splitting?
You developed a custom model by using Vertex AI to predict your application's user churn rate. You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort. What should you do?
Community Votes
68% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests whether you know that Vertex AI Model Monitoring can detect skew per feature from a single training table and that monitoring-config-from-file allows per-model configuration without separating data or creating separate endpoints.
For splitting traffic between two Vertex AI models while minimizing management effort, the community recommends deploying both models to the same endpoint and using monitoring-config-from-file. Keeping the original training dataset intact is preferred because Vertex AI Model Monitoring can analyze features individually for skew detection.
Choosing D, which separates the training dataset into two tables, because it seems logical to give each model its own training data. This is unnecessary for skew detection and adds management overhead, contradicting the requirement to minimize management effort.
Community Discussion (10 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because it minimizes management effort by deploying both models to a single endpoint and submitting one Vertex AI Model Monitoring job. The monitoring-config-from-file parameter lets you specify unique configurations for each model, including model IDs and feature selections, which is exactly what you need when traffic is split between two models. As a top-voted comment notes, it is not necessary to separate the training data into two tables because Vertex AI Model Monitoring can analyze each feature individually to detect skew. Another comment highlights that this approach reduces management overhead while still providing granular per-feature monitoring.Why the Other Options Are Wrong
Options A and C require two separate endpoints and two monitoring jobs, which increases management effort even though the same sampling rate and frequency are used. Option C also unnecessarily splits the training dataset into two tables. Option D uses a single endpoint but still separates the training dataset, which is unnecessary and contradicts the goal of minimal management effort. Furthermore, Option D’s monitoring-config-from-file is described as accounting for training datasets, while the correct approach—as in Option B—should account for model IDs and feature selections to leverage Vertex AI’s per-feature skew detection.Community Comment Notes
Several commenters initially chose D, arguing that separate training datasets are recommended for models trained on different feature sets. However, the majority recognized that Vertex AI Model Monitoring can operate on the original training dataset without splitting it. One commenter with many upvotes stated, 'Vertex AI Model Monitoring can indeed analyze each feature individually to detect skew,' directly refuting the need to separate data. Another comment emphasized reduced management effort as the key advantage of using a single endpoint and monitoring-config-from-file. The consensus is clear: don't split tables unless the monitoring requirement forces you to.Official Reference
Exam Strategy
When a question emphasizes minimizing management effort, always favor a single endpoint and a single monitoring job over separate resources. Also remember that Vertex AI Model Monitoring can detect skew per feature from an unsplit training table, so avoid selecting options that split the dataset unless there is an explicit technical need.
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monitoring-config-from-fileparameter lets you specify unique configurations for each model, including ID and training data information. This ensures targeted monitoring and analysis and a unique monitoring job.