Databricks Machine Learning Professional Question 16
Select 3You are working on a machine learning project where you need to store and manage features for a predictive model. You decide to use Databricks Feature Store. During your workflow, you update a feature table with new data and track the changes using Databricks Experiment Tracking. Which of the following actions are required to ensure the updated feature table is successfully used in downstream workflows?
- A
Overwrite the existing feature table with the updated data using the 'write_table()' method.
- B
Merge new feature data into the existing table using the 'merge()' method when overlapping records are found.
- C
Track the feature table version and changes in Databricks Experiment Tracking by logging the version of the feature table used in your experiment.
- D
Manually update all downstream workflows to reflect the new feature table version.
- E
Read the feature table using the 'read_table()' method in your machine learning pipeline to ensure the latest version is used.
Show answer and explanation
Correct answers: B, C, E
Explanation
To successfully use an updated feature table in downstream workflows, it is essential to effectively manage changes without overwriting data (using 'merge()'), track the version for reproducibility (using Experiment Tracking), and ensure the latest version is accessed by downstream workflows (using 'read_table()'). These practices align with Databricks Feature Store's capabilities to manage feature data effectively and integrate with ML workflows.
- A. Incorrect.
This option is incorrect because overwriting the feature table directly may lead to unintended data loss or inconsistencies, especially in collaborative environments. Merging data is a safer approach when dealing with updates.
- B. Correct.
This option is correct because merging new data into the existing feature table ensures that only relevant records are updated or added without overwriting the entire table.
- C. Correct.
This option is correct because tracking the feature table version in Databricks Experiment Tracking ensures reproducibility of experiments and allows others to verify the exact version of features used.
- D. Incorrect.
This option is incorrect because Databricks Feature Store integrates seamlessly with downstream workflows, and manual updates are not required as long as the correct version of the feature table is referenced.
- E. Correct.
This option is correct because reading the feature table with the 'read_table()' method ensures that the latest version of the feature table is used in your machine learning pipeline.