Google Professional Machine Learning Engineer Question 225
Select 4Google Cloud PlatformYou are tasked with training a machine learning model for predicting customer churn using Google Cloud. The dataset contains 10 million records and includes categorical, numerical, and text features. Which actions should you take during the model training process to ensure efficient and effective training?
- A
Use feature scaling for numerical features to ensure they are on a similar scale.
- B
Apply one-hot encoding or embeddings to categorical features to make them compatible with the model.
- C
Skip preprocessing text features since they can be fed directly into the model.
- D
Use a smaller subset of the dataset instead of the full dataset to avoid overfitting.
- E
Monitor training and validation metrics to detect overfitting or underfitting.
- F
Enable hyperparameter tuning using Vertex AI Vizier to optimize model performance.
Show answer and explanation
Correct answers: A, B, E, F
Explanation
To train an effective model, preprocessing steps such as feature scaling and encoding are essential. Monitoring metrics ensures that the model is neither overfitting nor underfitting. Additionally, hyperparameter tuning using tools like Vertex AI Vizier can significantly improve model performance. Preprocessing text features and using the full dataset (when feasible) are important for leveraging all available data and ensuring the model works effectively with all feature types.
- A. Correct.
Feature scaling is important for numerical features, especially for models sensitive to feature magnitudes (e.g., linear regression, neural networks). It ensures faster convergence and better model performance.
- B. Correct.
Categorical features need to be transformed (e.g., one-hot encoding or embeddings) for compatibility with most machine learning algorithms. Without this step, the model cannot process categorical data.
- C. Incorrect.
Skipping preprocessing of text features is incorrect. Text features need preprocessing (e.g., tokenization, embedding) to convert them into numerical representations before feeding them into a model.
- D. Incorrect.
Using a smaller subset of the dataset does not necessarily prevent overfitting and may lead to a loss of valuable information. Regularization techniques and monitoring metrics are better approaches.
- E. Correct.
Monitoring training and validation metrics is crucial for identifying potential overfitting (when training accuracy is much higher than validation accuracy) or underfitting (when both accuracies are low).
- F. Correct.
Hyperparameter tuning, such as using Vertex AI Vizier, helps optimize the model's performance by systematically searching for the best combination of hyperparameters.