Google Professional Machine Learning Engineer Question 55
Select 3Google Cloud PlatformYou are a Machine Learning Engineer working for an e-commerce company. You are tasked with building a product recommendation model using Google Cloud AutoML Tables. During the training process, which of the following considerations are important to ensure optimal performance and correct model behavior?
- A
Ensure the dataset is properly labeled and cleaned before uploading to AutoML Tables.
- B
Avoid including any categorical features in the dataset as AutoML cannot handle them.
- C
Split the dataset into training, validation, and test sets before starting the AutoML training process.
- D
Use a large and diverse dataset to improve the generalization of the model.
- E
Manually tune hyperparameters to achieve the best performance since AutoML does not support automatic hyperparameter optimization.
Show answer and explanation
Correct answers: A, C, D
Explanation
When using Google Cloud AutoML Tables, it is essential to ensure the quality and proper preparation of the dataset, including labeling and splitting into appropriate subsets. Additionally, providing a diverse dataset improves model generalization, and AutoML's built-in capabilities, such as handling categorical features and performing hyperparameter optimization, simplify the training process. These considerations help ensure optimal model performance and behavior during training and deployment.
- A. Correct.
Correct: Ensuring a properly labeled and cleaned dataset is critical for training any machine learning model, including AutoML. Poor-quality data can lead to suboptimal model performance.
- B. Incorrect.
Incorrect: AutoML Tables can handle categorical features and even automatically encode them for training. Avoiding categorical features unnecessarily limits the dataset.
- C. Correct.
Correct: Splitting the dataset into training, validation, and testing sets ensures the model evaluation is fair and prevents data leakage, which can inflate performance metrics.
- D. Correct.
Correct: A large and diverse dataset helps the model generalize better to unseen data, which can improve its performance in production.
- E. Incorrect.
Incorrect: AutoML automatically handles hyperparameter tuning as part of its training process. Manual tuning is not required.