Databricks Machine Learning Professional Question 18
Select 3You are working on a machine learning workflow where you manage features using the Databricks Feature Store. You need to ensure that your feature table contains the most up-to-date data without duplicating rows, and you want to track all relevant experiments linked to this feature table. Which of the following steps should you take to achieve this?
- A
Use the
mergeoperation in the Databricks Feature Store to update the feature table with new data. - B
Overwrite the feature table using the
overwritemethod to ensure all old data is replaced. - C
Log metadata about the feature table to an experiment using MLflow’s tracking API.
- D
Read the feature table into a pandas DataFrame using the
read_tablemethod for preprocessing. - E
Register the feature table with a specific model version to ensure reproducibility.
Show answer and explanation
Correct answers: A, C, E
Explanation
To manage features in a machine learning workflow effectively, you should use the merge operation to update the feature table without duplicating rows, log metadata about the feature table to MLflow for experiment tracking, and register the feature table with a specific model version to ensure reproducibility. These steps collectively help maintain an accurate feature store and robust experiment tracking.
- A. Correct.
Correct: The
mergeoperation in the Databricks Feature Store is designed to update feature tables with new data while avoiding duplication, which is essential for maintaining a clean and accurate feature store. - B. Incorrect.
Incorrect: While the
overwritemethod replaces all existing data, it is not appropriate in this scenario as it would delete older data and can lead to loss of historical information. - C. Correct.
Correct: Logging metadata about the feature table to an experiment using MLflow’s tracking API is a best practice for reproducibility and experiment tracking in machine learning workflows.
- D. Incorrect.
Incorrect: Reading the feature table into a pandas DataFrame is useful for preprocessing but does not contribute to updating or tracking features and is not relevant to the question's goals.
- E. Correct.
Correct: Registering the feature table with a specific model version ensures that the features used in training are reproducible and linked directly to the model version in question.