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Answer: Select the appropriate instance type based on the workload, enable auto-scaling, and use the library management system to install any additional required libraries.
Option B is the best approach as it takes into account the need for computational resources suitable for machine learning tasks, the ability to scale with auto-scaling, and the use of the library management system to ensure all required libraries are available without manual intervention.
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In a scenario where you need to scale your machine learning operations, you are considering the use of Databricks Runtime for Machine Learning. What are the key considerations you should take into account when creating a cluster with this runtime, and how would you ensure that all necessary Python libraries are available?
A
Focus only on the computational power of the cluster and assume that all libraries are pre-installed.
B
Select the appropriate instance type based on the workload, enable auto-scaling, and use the library management system to install any additional required libraries.
C
Create a cluster with the smallest instance type to save costs and install all libraries manually.
D
Use the default settings for the cluster and rely on the community to provide the necessary libraries.
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