Databricks Certified Data Engineer - Associate

Explanation

Cluster pools in Databricks are designed to provide pre-warmed, ready-to-use clusters that can start jobs faster than creating clusters from scratch. This is particularly useful when:

1. Speed is critical - Cluster pools maintain a set of pre-initialized clusters that can be assigned to jobs immediately
2. Reducing job startup latency - Instead of waiting for clusters to be created and configured, jobs can start running on pre-existing clusters
3. Performance optimization - For automated reports that need to be refreshed quickly, cluster pools eliminate the cluster startup overhead

Why other options are incorrect:

• A (version-controlled across multiple collaborators): Version control is handled by Databricks Repos, Git integration, or notebook versioning, not cluster pools.
• B (runnable by all stakeholders): Accessibility and permissions are managed through Databricks workspace permissions and Unity Catalog, not cluster pools.
• D (made reproducible): Reproducibility is achieved through notebook versioning, job configurations, and environment management, not cluster pools.

Key takeaway: Cluster pools are specifically designed to reduce job startup time by maintaining pre-warmed clusters, making them ideal for scenarios where reports need to be refreshed as quickly as possible.