Databricks Machine Learning Associate Question 66
Select 4While using the AutoML data exploration notebook in Databricks, which of the following attributes can be identified to better understand the dataset before model training?
- A
The distribution of values for numerical columns
- B
The correlation between features and the target variable
- C
The optimal machine learning algorithm for the dataset
- D
The presence of missing values in the dataset
- E
The percentage of duplicate rows in the dataset
Show answer and explanation
Correct answers: A, B, D, E
Explanation
The AutoML data exploration notebook in Databricks is designed to help users understand key attributes of their dataset, such as distributions, correlations, missing data, and duplicates. These insights allow users to prepare their data effectively before proceeding with model training. However, the notebook does not determine the optimal algorithm, as that step comes later during model development and tuning.
- A. Correct.
Correct: The AutoML data exploration notebook provides visualizations and descriptive statistics to analyze the distribution of numerical columns.
- B. Correct.
Correct: The notebook includes correlation analysis to help identify relationships between features and the target variable.
- C. Incorrect.
Incorrect: The optimal machine learning algorithm is not identified in the data exploration phase; this is determined during model training and evaluation.
- D. Correct.
Correct: The notebook highlights missing values, helping users assess data quality.
- E. Correct.
Correct: The notebook identifies duplicate rows and provides their percentage as part of data quality checks.