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Answer: Create a custom Docker image with all the required libraries and versions, then use this image to create the Databricks Runtime for Machine Learning cluster.
Option A is the recommended approach when dealing with multiple libraries with specific versions. Creating a custom Docker image ensures that the environment is consistent and reproducible, which is crucial for machine learning projects. The other options may not guarantee the same level of consistency and control over library versions.
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Your team is working on a machine learning project that requires the use of multiple Python libraries with specific versions. You need to create a Databricks Runtime for Machine Learning cluster that has all these libraries pre-installed. What is the recommended approach?
A
Create a custom Docker image with all the required libraries and versions, then use this image to create the Databricks Runtime for Machine Learning cluster.
B
Install each library individually in the cluster using the Databricks Library management system.
C
Instruct each team member to install the required libraries in their local development environment and push the code to Databricks.
D
Use the init script feature of Databricks to install the libraries upon cluster start.
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