Databricks Certified Data Engineer - Professional
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In a scenario where you are tasked with performing complex data transformations on a large dataset stored in Delta Lake within an Azure Databricks environment, you need to ensure the solution is efficient, scalable, and maintainable. The solution must also adhere to cost constraints and comply with organizational data governance policies. Considering these requirements, which of the following approaches would you choose to implement? (Choose one)
In a scenario where you are tasked with performing complex data transformations on a large dataset stored in Delta Lake within an Azure Databricks environment, you need to ensure the solution is efficient, scalable, and maintainable. The solution must also adhere to cost constraints and comply with organizational data governance policies. Considering these requirements, which of the following approaches would you choose to implement? (Choose one)
Explanation:
The correct answer is B because modularizing the data transformations into smaller, reusable functions and utilizing Apache Spark's distributed processing capabilities aligns with best practices for efficiency, scalability, and maintainability. This approach also supports cost optimization by efficiently utilizing cluster resources. Option A, while potentially reducing the number of jobs, can lead to complex, hard-to-maintain code. Option C is impractical for large datasets due to its inefficiency and lack of scalability. Option D introduces unnecessary complexity and potential maintainability issues by mixing multiple programming languages and tools.