Google Professional Machine Learning Engineer Question 487
Select 3Google Cloud PlatformYou are a Machine Learning Engineer responsible for maintaining an AI solution deployed on Google Cloud. Users report that the predictions from your model have become less accurate over time. Upon investigation, you discover that the input data distribution has shifted compared to the training data. Which actions should you take to address this issue?
- A
Implement data drift monitoring using Vertex AI Model Monitoring to detect changes in the input data distribution.
- B
Retrain the model using the latest data and compare its performance against the original model.
- C
Increase the size of the training dataset by adding more synthetic data generated using the original dataset.
- D
Deploy a new version of the model without further testing to quickly address the accuracy drop.
- E
Analyze the feature importance of the model to ensure that key features are still relevant in the current data.
Show answer and explanation
Correct answers: A, B, E
Explanation
When input data distribution shifts compared to the training data, it indicates a phenomenon called data drift. Addressing data drift involves monitoring for changes in the data, retraining the model with updated datasets, and ensuring that the model's features remain relevant. Implementing data drift monitoring, retraining the model with new data, and analyzing feature importance are all critical steps to maintain model performance and reliability. Actions like adding synthetic data or deploying new models without testing are either insufficient or risky in this context.
- A. Correct.
Implementing data drift monitoring using Vertex AI Model Monitoring is essential to detect and analyze changes in the input data distribution over time, allowing you to proactively address the issue.
- B. Correct.
Retraining the model using the latest data ensures that the model can adapt to the new data distribution and improves its accuracy. Comparing the new model's performance with the original helps validate the improvement.
- C. Incorrect.
While increasing the dataset size with synthetic data might help in some cases, it does not specifically address the issue of input data distribution shift. It could even introduce bias if not done carefully.
- D. Incorrect.
Deploying a new model version without proper testing is risky because it might not improve the situation or could even degrade the performance further. Testing is crucial in production environments.
- E. Correct.
Analyzing feature importance helps ensure that the features being used by the model are still relevant and meaningful in the context of the shifted data distribution, which can guide retraining and feature engineering efforts.