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Answer: Enable autoscaling with a wide range of minimum and maximum workers to dynamically adjust resources based on actual workload, balancing cost efficiency with performance scalability.
Option C is the correct choice because it leverages autoscaling to dynamically adjust the cluster size within a defined range, ensuring that costs are minimized during low usage periods while maintaining the ability to scale up for peak demands. This approach optimally balances cost and performance considerations, unlike the other options which either sacrifice performance for cost (A), cost for performance (B), or fail to adequately address workload variability (D).
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As a Databricks Certified Data Engineer, you are tasked with configuring a Databricks cluster to optimize for both cost and performance in a scenario where the workload varies significantly over time. The solution must adhere to strict budget constraints while ensuring that performance does not degrade during peak usage periods. Which of the following configurations would BEST meet these requirements? (Choose one option)
A
Select a smaller node type to minimize costs and manually adjust the cluster size based on anticipated workload changes, despite the potential for performance bottlenecks during unexpected spikes.
B
Choose a larger node type to guarantee high performance at all times, accepting higher fixed costs and the risk of underutilization during off-peak periods.
C
Enable autoscaling with a wide range of minimum and maximum workers to dynamically adjust resources based on actual workload, balancing cost efficiency with performance scalability.
D
Disable autoscaling and configure the cluster with a fixed number of medium-sized nodes to strike a balance between cost and performance, though this may not fully accommodate workload variability.
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