PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #262
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #262: Real Exam Question with Answer & Explanation
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Question
You are developing a training pipeline for a new XGBoost classification model based on tabular data. The data is stored in a BigQuery table. You need to complete the following steps: 1. Randomly split the data into training and evaluation datasets in a 65/35 ratio 2. Conduct feature engineering 3. Obtain metrics for the evaluation dataset 4. Compare models trained in different pipeline executions How should you execute these steps?
Options
- A1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets,
- B1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets,
- C1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as
- D1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as
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