Google Professional Machine Learning Engineer Question 364
Select 4Google Cloud PlatformYou are building a machine learning pipeline on Google Cloud to predict customer churn. After training your model on a large dataset, you need to validate the model and the data to ensure the results are reliable before deploying the model to production. Which of the following steps should you perform as part of the data and model validation process?
- A
Check for data skew between the training and validation datasets.
- B
Verify that the model's predictions on the training set achieve 100% accuracy.
- C
Use explainability tools like SHAP or AI Explanations to understand feature influence on predictions.
- D
Ensure that both training and validation datasets are representative of the production data distribution.
- E
Analyze the model's performance on an unseen test dataset or holdout set.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Data and model validation ensure that the model is robust, reliable, and generalizes well to the production environment. This involves detecting data skew, using explainability tools to verify that features are being used appropriately, ensuring representative datasets, and evaluating performance on unseen data. Focusing solely on training accuracy (e.g., aiming for 100%) is not a proper validation step, as it often indicates overfitting.
- A. Correct.
Data skew between the training and validation datasets can lead to poor generalization in production. Identifying and addressing skew is critical for model validation.
- B. Incorrect.
Achieving 100% accuracy on the training set is usually a sign of overfitting, not a meaningful validation step. It does not ensure the model will generalize well on unseen data.
- C. Correct.
Explainability tools like SHAP or AI Explanations help you understand feature importance, which is crucial for validating whether the model makes decisions based on relevant patterns.
- D. Correct.
Ensuring that the datasets are representative of the production data distribution helps avoid issues where the model performs poorly in production due to unexpected data patterns.
- E. Correct.
Evaluating the model's performance on an unseen test dataset or holdout set is a standard best practice for validating its generalization capabilities.