Google Professional Machine Learning Engineer Question 484
Select 3Google Cloud PlatformYour team has deployed a machine learning model for predicting customer churn into production on Google Cloud. After monitoring the model for a few weeks, you notice a significant drop in its accuracy on recent data. Which actions would you take to troubleshoot and address the issue?
- A
Perform data drift analysis to check if the input data distribution has changed compared to the training data.
- B
Retrain the model using the most recent production data without analyzing the root cause.
- C
Check for concept drift by investigating whether the relationship between input features and the target variable has changed.
- D
Examine the production logs to identify any anomalies or errors in data preprocessing during inference.
- E
Increase the model's confidence threshold to improve prediction accuracy.
Show answer and explanation
Correct answers: A, C, D
Explanation
When a model's accuracy drops in production, it is critical to identify and address the root cause to ensure consistent performance. Data drift, concept drift, and potential preprocessing issues are common culprits that must be investigated. Blindly retraining the model or making arbitrary adjustments like increasing the confidence threshold without proper analysis could lead to suboptimal outcomes.
- A. Correct.
Performing data drift analysis is a critical step in troubleshooting as changes in input data distribution could degrade model performance.
- B. Incorrect.
Retraining the model without understanding the root cause could propagate existing issues and is not a best practice.
- C. Correct.
Concept drift occurs when the relationship between features and the target variable changes over time, and identifying it is essential for addressing the accuracy drop.
- D. Correct.
Examining production logs can help detect issues such as errors in data preprocessing or incorrect input formats that might affect the model's performance.
- E. Incorrect.
Increasing the model's confidence threshold does not directly address the root cause of accuracy drop and could lead to other trade-offs, such as reduced recall.