Explanation:
Option B is the correct answer because it leverages the built-in Databricks Cost Management UI for a systematic and data-driven approach to cost optimization. This method allows for the identification of underutilized resources and idle clusters, enabling targeted optimizations such as adjusting cluster configurations and auto-scaling settings. This approach ensures cost reduction while maintaining the environment's scalability and performance, aligning with best practices for cloud cost management.
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In your role as a Databricks Certified Data Engineer - Professional, you are responsible for optimizing the cost of a Databricks environment that supports a variety of workloads, including data processing, machine learning, and ad-hoc analysis. The environment must remain scalable and performant while reducing costs. You need to analyze usage patterns, identify cost-saving opportunities, and implement changes to achieve cost optimization. Which of the following approaches is the BEST to meet these requirements? (Choose one option)
A
Manually review each Databricks cluster and notebook usage, making ad-hoc adjustments to cluster sizes and instance types based on workload requirements without a systematic analysis.
B
Utilize the Databricks Cost Management UI to systematically analyze usage patterns, identify underutilized clusters and idle resources, and optimize cluster configurations and auto-scaling settings based on data-driven insights.
C
Develop and implement a custom monitoring solution to track Databricks clusters and notebooks usage, then use this data to manually adjust cluster sizing, instance types, and auto-scaling settings.
D
Adopt a third-party cost optimization tool to analyze usage patterns and identify savings, relying solely on the tool's recommendations for implementing changes without further validation.