Databricks Machine Learning Associate Question 70
Select 4You are using the Databricks AutoML data exploration notebook to analyze a dataset before building a machine learning model. Which of the following key attributes of the dataset can be identified using the generated exploration outputs?
- A
The distribution of numerical features
- B
The presence of missing values in the dataset
- C
The optimal hyperparameters for a specific model
- D
Correlation between features
- E
The predicted target variable values for test data
- F
The data types of each column in the dataset
Show answer and explanation
Correct answers: A, B, D, F
Explanation
The Databricks AutoML data exploration notebook is designed to provide insights into the dataset before model training. It covers key attributes such as feature distributions, missing values, correlations, and data types. However, it does not handle tasks like hyperparameter tuning or making predictions, as those are part of model training and inference workflows.
- A. Correct.
Correct: The AutoML data exploration notebook provides visualizations and statistics that help understand the distribution of numerical features.
- B. Correct.
Correct: The data exploration notebook highlights missing values in the dataset, allowing users to identify data quality issues.
- C. Incorrect.
Incorrect: Hyperparameter tuning is not part of the data exploration process; it is performed during the model training phase.
- D. Correct.
Correct: The notebook includes correlation matrices or heatmaps to show relationships between features.
- E. Incorrect.
Incorrect: Predicting target variable values is part of model inference, not data exploration.
- F. Correct.
Correct: The data exploration notebook identifies the data types (e.g., integer, float, string) for each column in the dataset.