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Answer: Choose a node type with a larger amount of memory per node, as this directly addresses the memory requirements of the workload without unnecessarily increasing costs with additional CPU cores.
Option B is the correct choice because it directly addresses the memory requirements of the workload by selecting a node type with a larger amount of memory, which is crucial for memory-intensive tasks. This approach avoids the unnecessary cost of additional CPU cores that the workload does not benefit from. Options A and D introduce unnecessary complexity and cost by focusing on CPU cores or a mix of node types, which do not align with the workload's needs. Option C is incorrect because disk I/O performance does not compensate for insufficient memory in memory-intensive workloads.
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As a Databricks Certified Data Engineer, you are tasked with optimizing a Databricks cluster for memory-intensive workloads in a cost-sensitive environment. The workload involves processing large datasets that require significant memory resources but do not benefit significantly from additional CPU cores. Given these constraints, which of the following configurations would you choose to best meet the workload's requirements while also considering cost efficiency? (Choose one option)
A
Select a node type with a higher number of CPU cores and enable autoscaling to dynamically adjust resources, ensuring both memory and CPU are optimized for varying workloads.
B
Choose a node type with a larger amount of memory per node, as this directly addresses the memory requirements of the workload without unnecessarily increasing costs with additional CPU cores.
C
Configure the cluster to use a custom disk type with higher I/O performance, under the assumption that disk speed will compensate for memory limitations.
D
Implement a combination of node types, using some with high memory for memory-intensive tasks and others with high CPU for processing tasks, to balance cost and performance.
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