Databricks Data Engineer Associate exam dumps

Databricks Data Engineer Associate practice question 198 of 532

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

Databricks Data Engineer Associate Question 198

Single answer

You are working with a Databricks notebook and have defined a custom Python function named process_data to clean and process a dataset. You want to identify the location where this function is accessible within your Databricks workspace. Which of the following statements accurately describes the function's location?

  1. A

    The function is available only within the notebook where it is defined.

  2. B

    The function is available across all notebooks in the same workspace.

  3. C

    The function is available globally across all Databricks workspaces linked to your account.

  4. D

    The function is automatically saved to the Databricks Repos for future use.

Show answer and explanation

Correct answer: A

Explanation

In Databricks, functions defined within a notebook are scoped locally to that notebook. They are not accessible in other notebooks unless explicitly exported or saved to a shared library. This behavior ensures that notebook-specific code is isolated unless shared intentionally.

  • A. Correct.

    This is correct because a function defined in a Databricks notebook is scoped to that specific notebook and cannot be accessed in other notebooks unless explicitly exported.

  • B. Incorrect.

    This is incorrect because Databricks does not automatically make notebook-defined functions accessible across all notebooks in the same workspace.

  • C. Incorrect.

    This is incorrect because functions defined in a notebook are not shared globally across all Databricks workspaces linked to your account.

  • D. Incorrect.

    This is incorrect as functions defined in a notebook are not automatically saved to Databricks Repos. To save such functions for reuse, you need to explicitly move them to a shared library or code repository.

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