Databricks Certified Data Engineer - Associate

Explanation

Cluster pools in Databricks are designed to provide pre-warmed, ready-to-use clusters that can start jobs quickly without the overhead of cluster creation and initialization time. This makes them ideal for scenarios where speed and performance are critical.

Let's analyze each option:

A. An automated report needs to be version-controlled across multiple collaborators.

• Version control is handled by Databricks Repos or Git integration, not by cluster pools.

B. An automated report needs to be runnable by all stakeholders.

• Accessibility for stakeholders is managed through permissions and access controls, not cluster pools.

C. An automated report needs to be refreshed as quickly as possible. ✅

• CORRECT: Cluster pools maintain pre-warmed clusters that can start jobs immediately, reducing the time needed to refresh reports. This eliminates the cluster startup time (which can take several minutes) and provides faster job execution.

D. An automated report needs to be made reproducible.

• Reproducibility is achieved through consistent environment configuration, notebook versioning, and data versioning, not specifically through cluster pools.

Key Benefits of Cluster Pools:

1. Reduced startup time: Pre-warmed clusters eliminate the time needed to provision and configure resources
2. Cost efficiency: While clusters are idle in the pool, they consume minimal resources
3. Consistent performance: Same cluster configuration ensures consistent execution times
4. Better resource utilization: Multiple jobs can share the same pool of clusters

Therefore, when an automated report needs to be refreshed as quickly as possible, cluster pools are the optimal choice because they minimize the latency between job submission and execution.