Databricks Machine Learning Associate Question 81
Select 3You are tasked with storing a processed dataset into a Databricks Feature Store table to facilitate feature reuse across multiple machine learning models. Which of the following steps must you take to successfully write data to the feature store table?
- A
Define a unique primary key for the feature store table.
- B
Use the
write_tablemethod from the Feature Store client to write the data. - C
Ensure the data being written is in a Delta table format.
- D
Register the feature store table to the Databricks MLflow model registry before writing the data.
- E
Specify a timestamp column to track feature changes over time.
Show answer and explanation
Correct answers: A, B, E
Explanation
Writing data to a Databricks Feature Store table requires a unique primary key for identification, the use of the write_table method to store the features, and optionally a timestamp column to track feature changes over time. These steps ensure data integrity and usability of the feature store. Delta table format is not a strict requirement, and registering the feature table in the MLflow model registry is unrelated to the data writing process.
- A. Correct.
Defining a unique primary key is required to ensure that each row in the feature store table is uniquely identified, which is critical for feature lookups.
- B. Correct.
The
write_tablemethod provided by the Feature Store client is necessary for writing data into the Feature Store table in Databricks. - C. Incorrect.
While Delta table format is commonly used in Databricks, it is not a strict requirement for writing data to the feature store table. The Feature Store can handle other formats as well.
- D. Incorrect.
Registering the feature store table to the Databricks MLflow model registry is not required for writing the data. The MLflow model registry is used for managing models, not feature tables.
- E. Correct.
Specifying a timestamp column is optional but highly recommended for tracking feature changes over time, enabling point-in-time correctness for training and inference.