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How can you optimize batch processing job execution times in Azure Databricks through advanced resource monitoring?
A
Implementing a custom metrics dashboard using Azure Log Analytics to aggregate job execution times and cluster metrics for bottleneck identification
B
Configuring Azure Databricks to automatically adjust cluster sizes and configurations based on job execution patterns and resource utilization metrics
C
Leveraging Azure Monitor‘s application insights to track job execution metrics and using Azure Machine Learning for optimization opportunities identification
D
Using the Databricks Spark UI exclusively for job and cluster performance monitoring, with manual resource adjustments based on observations
