Databricks Machine Learning Associate Question 48
Select 3A data science team is using Databricks AutoML to streamline their machine learning workflow. Which of the following steps are automatically completed by AutoML during the workflow?
- A
Feature engineering, such as handling missing values and encoding categorical variables
- B
Hyperparameter tuning for the generated models
- C
Performing exploratory data analysis (EDA) to generate insights about the dataset
- D
Deploying the trained model to a production endpoint
- E
Selecting and training multiple machine learning models on the dataset
Show answer and explanation
Correct answers: A, B, E
Explanation
Databricks AutoML automates critical steps of the machine learning workflow, including feature engineering, model selection, training, and hyperparameter tuning. However, it does not perform full exploratory data analysis or handle model deployment, as these steps often require human intervention or are part of a different pipeline.
- A. Correct.
Databricks AutoML can handle feature engineering tasks, including imputing missing values and encoding categorical variables, as part of its automated pipeline.
- B. Correct.
Databricks AutoML performs hyperparameter tuning to optimize the generated models. This is a key feature of AutoML systems.
- C. Incorrect.
While Databricks AutoML provides visualizations and insights about the dataset, it does not perform a complete exploratory data analysis (EDA) as this typically requires manual intervention and domain knowledge.
- D. Incorrect.
Databricks AutoML does not handle deployment of models to production endpoints. Deployment is a separate step that needs to be managed by the user.
- E. Correct.
Databricks AutoML automatically selects suitable machine learning algorithms and trains multiple models on the dataset.