Databricks Machine Learning Associate Question 67
Select 4While using the Databricks AutoML data exploration notebook, you are analyzing a dataset to prepare it for a machine learning pipeline. Which of the following are key attributes of the dataset that the notebook helps you identify?
- A
The distribution of values in each column
- B
Correlations between numerical features
- C
The number of missing values in each column
- D
Real-time model performance metrics
- E
Duplicate rows in the dataset
Show answer and explanation
Correct answers: A, B, C, E
Explanation
The AutoML data exploration notebook is designed to help users analyze and understand their dataset before building a machine learning model. It provides insights into key attributes such as data distribution, feature correlations, missing values, and duplicates. However, real-time model performance metrics are not included in this phase, as they pertain to model evaluation rather than data exploration.
- A. Correct.
The AutoML data exploration notebook provides visualizations and summary statistics to help you understand the distribution of values in each column, which is critical for feature engineering.
- B. Correct.
The notebook calculates and visualizes correlations between numerical features to identify relationships that may influence model performance.
- C. Correct.
The notebook highlights missing values in the dataset, allowing you to determine which columns need imputation or removal.
- D. Incorrect.
Real-time model performance metrics are not part of the data exploration phase; they are relevant during model training and evaluation.
- E. Correct.
The notebook can identify duplicate rows as part of its data quality checks, which is essential for cleaning the dataset.