Databricks Certified Data Engineer - Associate

Explanation

Job clusters are designed for automated, scheduled workloads, while all-purpose clusters are intended for interactive development and ad-hoc analysis.

Let's analyze each option:

A. An ad-hoc analytics report needs to be developed while minimizing compute costs.

• ❌ Incorrect - Ad-hoc analytics reports are interactive workloads best suited for all-purpose clusters. While minimizing costs is important, job clusters aren't designed for interactive development.

B. A data team needs to collaborate on the development of a machine learning model.

• ❌ Incorrect - Collaborative development requires interactive sessions where multiple users can work together, which is the purpose of all-purpose clusters.

C. An automated workflow needs to be run every 30 minutes.

• ✅ Correct - Job clusters are specifically designed for automated, scheduled workflows. They start when a job runs and terminate when the job completes, making them cost-effective for scheduled tasks.

D. A Databricks SQL query needs to be scheduled for upward reporting.

• ✅ Correct - Scheduled SQL queries for reporting are automated workflows that should use job clusters. This is a common use case for job clusters in production environments.

E. A data engineer needs to manually investigate a production error.

• ❌ Incorrect - Manual investigation requires interactive exploration and debugging, which is best done with an all-purpose cluster.

Key Differences:

• Job Clusters: Created when a job starts, terminated when the job completes. Best for automated, scheduled, production workloads.
• All-Purpose Clusters: Long-running clusters for interactive development, collaboration, and ad-hoc analysis.

Best Practices:

1. Use job clusters for production pipelines, scheduled reports, and automated workflows
2. Use all-purpose clusters for development, testing, debugging, and collaborative work
3. Job clusters are more cost-effective for scheduled tasks because they only run when needed
4. All-purpose clusters provide persistent environments for ongoing work