Google Professional Machine Learning Engineer Question 486
Select 3Google Cloud PlatformYou have deployed a machine learning model for predicting customer churn on Google Cloud. After several weeks, you notice a drop in the model's accuracy and suspect data drift may be the cause. Which actions should you take to monitor and address this issue effectively?
- A
Set up Vertex AI Model Monitoring to track feature skew and distribution changes over time.
- B
Re-train the model immediately using the most recent data without analyzing the root cause of drift.
- C
Analyze input features and prediction distributions to identify if specific features have shifted.
- D
Deploy a rule-based system to replace the machine learning model to avoid future drift issues.
- E
Review training and serving pipelines to ensure data preprocessing steps are consistent.
Show answer and explanation
Correct answers: A, C, E
Explanation
Data drift occurs when the data used in production differs significantly from the data the model was trained on, leading to performance degradation. To address this, it is essential to monitor the model using tools like Vertex AI Model Monitoring, analyze the changes in data distributions, and ensure consistency between training and serving pipelines. These actions help identify and mitigate the root causes of data drift effectively.
- A. Correct.
Setting up Vertex AI Model Monitoring is a recommended practice in Google Cloud to track feature skew and distribution changes, which are common indicators of data drift.
- B. Incorrect.
Re-training the model immediately without understanding the root cause is not considered best practice. It may lead to wasted resources and does not guarantee the issue will be resolved.
- C. Correct.
Analyzing input features and prediction distributions can help identify which specific features or aspects of the data are causing the drift, enabling targeted fixes.
- D. Incorrect.
Deploying a rule-based system is not an appropriate solution for addressing data drift in a machine learning workflow. It contradicts the purpose of using machine learning models for dynamic data.
- E. Correct.
Reviewing training and serving pipelines ensures that preprocessing steps are consistent across both stages, which is critical for minimizing issues caused by data drift.