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Answer: Organize Python and Scala code into separate cells, utilizing language-specific libraries and functions to leverage each language's strengths fully.
The best practice for leveraging multi-language support in Databricks notebooks is to organize code into separate cells by language, allowing for the use of language-specific libraries and functions. This approach maximizes the strengths of each language while maintaining code clarity and organization. Combining languages in a single cell (Option A) can lead to errors and confusion. Avoiding language-specific features (Option B) limits the functionality and benefits of using multiple languages. Converting code to a single language (Option D) is unnecessary and adds complexity, as Databricks inherently supports multi-language notebooks efficiently.
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In a Databricks environment, you are tasked with developing a notebook that processes a large dataset requiring both Python for data visualization and Scala for data transformation. The solution must adhere to best practices for multi-language support, ensuring code maintainability and efficiency. Which of the following approaches BEST meets these requirements? (Choose one option.)
A
Combine Python and Scala code within the same cell to minimize the number of cells, despite the potential for confusion and errors.
B
Separate Python and Scala code into distinct cells but avoid using any language-specific libraries or functions to ensure compatibility.
C
Organize Python and Scala code into separate cells, utilizing language-specific libraries and functions to leverage each language's strengths fully.
D
Convert all code to a single language using an external tool before execution to simplify the notebook's language requirements.
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