Databricks Certified Data Engineer - Associate
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In a data engineering project using Databricks, you are tasked with optimizing the performance of a Spark SQL query that frequently calculates the square root of values in a column for analytical purposes. The solution must be reusable across multiple queries and adhere to best practices for UDF (User-Defined Function) implementation in Spark SQL. Considering the need for performance optimization, reusability, and adherence to Spark SQL UDF best practices, which of the following approaches should you choose?
Explanation:
Option B is correct as it demonstrates the proper way to define and use a SQL UDF in Spark SQL, which is reusable and adheres to best practices. Option C is also correct because Python UDFs (pandas_udf) can offer better performance for certain numerical computations, making it a viable option depending on project requirements. Option A is incorrect because while built-in functions are performant, they do not meet the requirement for reusability across multiple queries. Option D is incorrect because precomputing values increases storage requirements and does not offer the flexibility of runtime calculations. Option E is provided to test the understanding of when each approach (B or C) might be more appropriate, making it a correct choice when the scenario allows for either solution based on specific constraints.