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Answer: Utilizing Azure Machine Learning to model and predict workload patterns based on historical Databricks job and cluster metrics, applying scaling decisions via Azure Automation
The most suitable approach for proactively scaling Azure Databricks clusters based on predictive analysis of workload patterns is to utilize Azure Machine Learning. This method allows for the creation of models that analyze historical Databricks job and cluster metrics to predict future workload patterns. These predictions enable informed decisions about scaling the clusters, with Azure Automation applying these decisions dynamically. This ensures resources are efficiently managed, scaling up or down as needed to meet workload demands. Other options, such as manual scaling or using predefined metrics without predictive analysis, lack the efficiency and accuracy of machine learning models. Custom scripts, while providing some automation, do not offer the robustness of predictive analysis.
Author: LeetQuiz Editorial Team
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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?
A
Manually scaling clusters based on expected workload increases, guided by past trends observed in Azure Monitor
B
Configuring Azure Autoscale based on predefined metrics and thresholds without predictive analysis
C
Utilizing Azure Machine Learning to model and predict workload patterns based on historical Databricks job and cluster metrics, applying scaling decisions via Azure Automation
D
Implementing custom scripts in Azure Databricks to adjust cluster size on-the-fly based on current job queue lengths and historical execution times
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