Answer: timeout_minutes

The correct answer is **B. timeout_minutes**. This parameter is specifically designed to set a time limit for AutoML runs, ensuring they don't exceed a specified duration. It's crucial for managing resource usage and scheduling, especially in shared environments or when dealing with time constraints. - **max_trials**: Controls the maximum number of model training attempts, but doesn't directly limit time. - **exclude_cols**: Specifies columns to exclude from training, but doesn't affect duration. - **pos_label**: Identifies the positive class label for binary classification, but has no impact on run time. **Example Usage**: ```python from databricks import automl automl_classifier = automl.classify( target_col="label", data=data, timeout_minutes=60 # Set a 60-minute timeout ) ``` **Key Points**: - **Managing Run Duration**: `timeout_minutes` is essential for controlling AutoML run time and preventing excessive resource consumption. - **Resource Management**: It's a valuable tool for optimizing resource usage and ensuring efficient model building processes. - **Scheduling Considerations**: It's also helpful for accommodating time constraints and fitting AutoML runs into specific schedules.

Author: LeetQuiz Editorial Team