Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: D - They can use clusters that are from a cluster pool

Why this is correct:

1. Cluster Pools: When you use clusters from a cluster pool, Databricks maintains a pool of pre-warmed, idle instances that are ready to be assigned to clusters. This significantly reduces cluster start-up time because:

   • The instances are already provisioned and warmed up
   • No need to wait for cloud provider instance provisioning
   • The instances are already configured and ready to use

2. Job Clusters vs. All-Purpose Clusters (Option B): While job clusters are optimized for jobs, they still need to be provisioned from scratch each time unless they come from a pool. Using job clusters alone doesn't solve the slow start-up problem.

3. Single-Node Clusters (Option C): Configuring clusters to be single-node might reduce some complexity, but it doesn't address the fundamental issue of instance provisioning time.

4. Autoscaling (Option E): Autoscaling helps with handling varying workloads but doesn't improve initial cluster start-up time.

5. Databricks SQL Endpoints (Option A): These are for SQL analytics workloads, not for general Spark jobs, and don't address cluster start-up time for job tasks.

Best Practice: For production jobs that run regularly (like nightly jobs), using cluster pools is a recommended best practice to minimize cluster start-up latency and ensure consistent performance.