Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 433 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 433

Select 3

You are working on a machine learning project in Databricks and have a preprocessed DataFrame containing features you want to store in the Databricks Feature Store. Which of the following steps are required to write data to a feature store table?

  1. A

    Initialize a FeatureStoreClient instance to interact with the feature store.

  2. B

    Ensure the DataFrame includes a unique primary key column for each record.

  3. C

    Use the write_table method of the FeatureStoreClient to save the DataFrame.

  4. D

    Register the feature store table in a Delta Lake database before writing data.

  5. E

    Convert the DataFrame to a Spark SQL table before writing to the feature store.

Show answer and explanation

Correct answers: A, B, C

Explanation

To write data to a Databricks Feature Store table, you must use the FeatureStoreClient, which provides the necessary methods for interacting with the feature store. The DataFrame should contain a unique primary key column to identify records, and the write_table method is used to save the data. Additional steps like manual Delta Lake registration or converting to a Spark SQL table are not required.

  • A. Correct.

    Correct. Initializing a FeatureStoreClient instance is required to interact with the Databricks Feature Store API.

  • B. Correct.

    Correct. A unique primary key column is essential to identify individual feature records in the feature store.

  • C. Correct.

    Correct. The write_table method of the FeatureStoreClient is used to write the DataFrame to a feature store table.

  • D. Incorrect.

    Incorrect. While the feature store table is stored in Delta format, you do not need to register it in a Delta Lake database manually before writing data; this is handled automatically by the Feature Store.

  • E. Incorrect.

    Incorrect. There is no need to convert the DataFrame to a Spark SQL table before writing it to the feature store. The FeatureStoreClient can directly accept a DataFrame.

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