You are creating a retraining policy for a customer churn prediction model deployed in Vertex AI. New training data is added weekly. You want to implement a model retraining process that minimizes…

The correct answer is A. Retrain the model when a significant shift in the distribution of customer attributes is detected in. Retraining based on detected significant data distribution shifts ensures the model stays relevant and accurate while minimizing unnecessary retraining costs and effort. This approach is more efficient than retraining on a fixed schedule or solely based on accuracy metrics.

Submitted by yuriko_h· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

You are creating a retraining policy for a customer churn prediction model deployed in Vertex AI. New training data is added weekly. You want to implement a model retraining process that minimizes cost and effort. What should you do?

Options

ARetrain the model when a significant shift in the distribution of customer attributes is detected in
BRetrain the model when the model's latency increases by 10% due to increased traffic.
CRetrain the model when the model accuracy drops by 10% on the new training dataset.
DRetrain the model every week when new training data is available.

How the community answered

(33 responses)

A
58% (19)
B
6% (2)
C
24% (8)
D
12% (4)

Explanation

Retraining based on detected significant data distribution shifts ensures the model stays relevant and accurate while minimizing unnecessary retraining costs and effort. This approach is more efficient than retraining on a fixed schedule or solely based on accuracy metrics.

Topics

#Model Retraining#Data Drift Detection#MLOps#Model Monitoring

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