Databricks Machine Learning Associate Question 68
Select 4You are using the Databricks AutoML data exploration notebook to analyze a dataset for a machine learning project. Which of the following attributes can be identified directly using the visualizations and outputs generated by the AutoML data exploration notebook?
- A
Distribution of numerical features
- B
Presence of missing values in the dataset
- C
Optimal hyperparameters for the model
- D
Correlation between features
- E
Model accuracy metrics
- F
Cardinality of categorical features
Show answer and explanation
Correct answers: A, B, D, F
Explanation
The Databricks AutoML data exploration notebook is designed to provide key insights about the dataset before model training begins. It focuses on data quality, feature characteristics, and relationships within the data, such as distributions, missing values, correlations, and cardinality of categorical features. However, it does not include outputs related to model training, such as hyperparameters or accuracy metrics.
- A. Correct.
The AutoML data exploration notebook provides visualizations such as histograms, which allow you to observe the distribution of numerical features.
- B. Correct.
The notebook includes summaries of missing values for each column, helping you identify data quality issues.
- C. Incorrect.
Determining optimal hyperparameters for a model is part of the training process, not the data exploration phase.
- D. Correct.
The notebook generates correlation matrices or heatmaps, which help in identifying relationships between features.
- E. Incorrect.
Model accuracy metrics are produced after training, not during the data exploration phase.
- F. Correct.
The AutoML data exploration notebook provides insights about the cardinality of categorical features, such as the number of unique values in each category.