Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 76 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 76

Select 3

You are tasked with creating a feature store table in Databricks to standardize and share features for a machine learning project. Which of the following steps are required to successfully create a feature store table?

  1. A

    Define a feature table schema using a Spark DataFrame.

  2. B

    Use the FeatureStoreClient.create_table method to register the feature table.

  3. C

    Manually write the feature table data to a Delta table without using the Feature Store API.

  4. D

    Provide a unique name for the feature table in the format 'database_name.table_name'.

  5. E

    Train a machine learning model before creating the feature table.

Show answer and explanation

Correct answers: A, B, D

Explanation

Creating a feature store table in Databricks involves defining the schema using a Spark DataFrame, registering the table with the Feature Store API via the FeatureStoreClient.create_table method, and providing a unique name for the table. Writing data manually without the Feature Store API or training a model beforehand is not required.

  • A. Correct.

    Correct. A Spark DataFrame is required to define the schema and data for the feature table before registering it in the feature store.

  • B. Correct.

    Correct. The FeatureStoreClient.create_table method is used to register the feature table and associate it with the feature store.

  • C. Incorrect.

    Incorrect. Writing data manually to a Delta table without the Feature Store API does not integrate the table with Databricks Feature Store.

  • D. Correct.

    Correct. A unique name in the format 'database_name.table_name' is required to register the feature table in the feature store.

  • E. Incorrect.

    Incorrect. Training a machine learning model is not a prerequisite for creating a feature table in the feature store.

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