Google Professional Machine Learning Engineer

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Answer: 1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare pipeline runs in Vertex AI Experiments.

Option A is the correct answer because it leverages Vertex AI Pipelines and Vertex AI Experiments, providing a flexible and controlled environment for data splitting, feature engineering, and model training. Vertex AI autologging simplifies capturing evaluation metrics during training, and Vertex AI Experiments offer a centralized interface to compare metrics across different pipeline runs. In comparison, options C and D, which use BigQuery ML, provide less control over feature engineering and model comparisons. Option B, while also using Vertex AI Pipelines, recommends using Vertex ML Metadata for model comparison, which is less comprehensive than Vertex AI Experiments.

Author: LeetQuiz Editorial Team

Quick Answer

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Author: LeetQuiz Editorial Team

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You are tasked with developing a machine learning pipeline for training an XGBoost classification model using tabular data stored in a BigQuery table. Your goal is to ensure that the pipeline effectively handles data splitting, feature engineering, and model evaluation, and allows for easy comparison of different models. The required steps are: 1. Randomly split the data into training and evaluation datasets in a 65/35 ratio. 2. Conduct feature engineering to prepare the data for training. 3. Obtain evaluation metrics for the model's performance. 4. Compare the performance of models trained in different pipeline executions. Which approach should you take to achieve these requirements?

Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare pipeline runs in Vertex AI Experiments.

52.8%

Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare models using the artifacts’ lineage in Vertex ML Metadata.

11.4%

In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use a SQL view to apply feature engineering and train the model using the data in that view. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.

16.3%

In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use ML TRANSFORM to specify the feature engineering transformations and train the model using the data in the table. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.

19.5%