Answer: An automated workflow needs to be run every 30 minutes.

## Explanation **Job clusters** are designed for automated, scheduled workflows and production jobs, while **all-purpose clusters** are intended for interactive development and collaboration. Let's analyze each option: - **A. An ad-hoc analytics report needs to be developed while minimizing compute costs.** ❌ This describes interactive development work, which is better suited for all-purpose clusters. Job clusters are not ideal for ad-hoc work. - **B. A data team needs to collaborate on the development of a machine learning model.** ❌ This is collaborative development work, which requires an all-purpose cluster for interactive notebook development and team collaboration. - **C. An automated workflow needs to be run every 30 minutes.** ✅ This is the perfect use case for Job clusters. Automated, scheduled workflows benefit from Job clusters because they: - Start automatically when the job runs - Terminate automatically when the job completes - Are more cost-effective for scheduled workloads - Provide better isolation for production jobs - **D. A Databricks SQL query needs to be scheduled for upward reporting.** ✅ This is also a good use case for Job clusters. Scheduled SQL queries for reporting purposes fit the Job cluster model well. - **E. A data engineer needs to manually investigate a production error.** ❌ This requires interactive investigation and debugging, which is better suited for all-purpose clusters where the engineer can run commands interactively. **Key differences between Job clusters and All-purpose clusters:** - **Job clusters**: Automated workflows, scheduled jobs, production pipelines - **All-purpose clusters**: Interactive development, collaboration, ad-hoc analysis, debugging The primary correct answer is **C** because it clearly describes an automated, scheduled workflow that runs frequently, which is the ideal scenario for Job clusters.

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