Databricks Certified Data Engineer - Associate
Get started today
Ultimate access to all questions.
In your role as a Data Engineer at a company utilizing Azure Databricks for big data analytics, you are tasked with integrating a custom library not available in the default Databricks environment into your project. The library must be compatible with the Databricks runtime, and the solution should minimize maintenance overhead and ensure scalability across multiple clusters. Considering these requirements, which of the following approaches is the BEST to achieve this goal? Choose one option.
In your role as a Data Engineer at a company utilizing Azure Databricks for big data analytics, you are tasked with integrating a custom library not available in the default Databricks environment into your project. The library must be compatible with the Databricks runtime, and the solution should minimize maintenance overhead and ensure scalability across multiple clusters. Considering these requirements, which of the following approaches is the BEST to achieve this goal? Choose one option.
Explanation:
The Databricks library management feature (Option C) is the most efficient and scalable solution for integrating a custom library into a Databricks project. It ensures compatibility with the Databricks runtime, simplifies maintenance by centralizing library management, and supports scalability across multiple clusters. Manual installation (Option A) is prone to errors and does not scale well. Using a custom Docker image (Option B) introduces unnecessary complexity and management overhead. Rewriting the library's source code (Option D) is impractical, especially for third-party libraries, and does not guarantee compatibility or ease of maintenance.