How can you dynamically allocate resources to Apache Spark jobs in Databricks to optimize processing time and cost when the jobs have varying computational requirements?

Real Exam

Writing custom logic within each Spark job to request additional resources from the Databricks REST API based on runtime metrics

4.6%

Configuring each Spark job with a static allocation of resources based on peak usage estimates to ensure availability

3.1%

Utilizing Databricks pools to pre-allocate a shared set of resources that Spark jobs can dynamically acquire as needed

15.4%

Leveraging Databricks' autoscaling feature within Spark clusters to dynamically adjust resources based on the workload

76.9%

Databricks Certified Data Engineer - Professional