Databricks Certified Data Engineer - Associate

Explanation

Cluster pools in Databricks are designed to reduce cluster startup times by maintaining a pool of pre-configured, idle clusters that are ready to be assigned to users or jobs. This is particularly useful for scenarios where speed and responsiveness are critical.

Let's analyze each option:

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

• Cluster pools significantly reduce cluster startup time because clusters are already pre-warmed and ready to use
• This minimizes the latency between job submission and execution, making report refreshes faster

B. An automated report needs to be made reproducible. ❌

• Reproducibility is achieved through version control, notebook revision history, and consistent environment configuration, not cluster pools

C. An automated report needs to be tested to identify errors. ❌

• Testing and error identification are development/testing processes that don't specifically require cluster pools

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

• Version control is handled through Databricks Repos, Git integration, and notebook revision tracking

E. An automated report needs to be runnable by all stakeholders. ❌

• Accessibility and permissions are managed through Databricks workspace permissions and access controls, not cluster pools

Key Takeaway: Cluster pools optimize for performance and reduced startup latency, making them ideal for time-sensitive operations like quick report refreshes where minimizing wait time is crucial.