To effectively manage costs and performance by proactively scaling your Azure Databricks clusters based on predictive analysis of workload patterns, which approach should you adopt?

Real Exam

Manually scaling clusters based on expected workload increases, guided by past trends observed in Azure Monitor

3.1%

Configuring Azure Autoscale based on predefined metrics and thresholds without predictive analysis

6.3%

Utilizing Azure Machine Learning to model and predict workload patterns based on historical Databricks job and cluster metrics, applying scaling decisions via Azure Automation

84.4%

Implementing custom scripts in Azure Databricks to adjust cluster size on-the-fly based on current job queue lengths and historical execution times

6.3%

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To effectively manage costs and performance by proactively scaling your Azure Databricks clusters based on predictive analysis of workload patterns, which approach should you adopt?