Databricks Certified Data Engineer - Associate

Explanation

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

Why this is correct:

Cluster pools (also known as instance pools) significantly reduce cluster startup time because they maintain a pool of pre-warmed, idle instances that are ready to be used. When a job needs to start a cluster, it can pull instances from this pool rather than waiting for new instances to be provisioned from scratch, which involves:

1. Requesting instances from the cloud provider
2. Waiting for instance allocation
3. Installing Databricks runtime and dependencies
4. Initializing the cluster

With cluster pools, steps 1-3 are already completed, so clusters start much faster.

Analysis of other options:

A. They can use endpoints available in Databricks SQL - Incorrect. Databricks SQL endpoints are for SQL analytics workloads, not for improving cluster startup time for jobs. This doesn't address the cluster startup issue.

B. They can use jobs clusters instead of all-purpose clusters - Partially correct but not the best answer. While jobs clusters are optimized for job execution and can be terminated after job completion, they don't inherently start faster than all-purpose clusters. The startup time depends on whether they're created from scratch or from a pool.

C. They can configure the clusters to be single-node - Incorrect. Single-node clusters might start slightly faster than multi-node clusters because there's only one node to initialize, but the improvement is minimal compared to using a cluster pool. The main bottleneck is the initial provisioning and setup time, not the number of nodes.

E. They can configure the clusters to autoscale for larger data sizes - Incorrect. Autoscaling helps with runtime performance by adjusting cluster size based on workload, but it doesn't improve cluster startup time. In fact, autoscaling might add overhead as the cluster needs to monitor workload and add/remove nodes.

Additional context:

• Cluster pools are specifically designed to reduce cluster startup latency
• They maintain a warm pool of instances with Databricks runtime pre-installed
• This is particularly beneficial for jobs that run frequently (like nightly jobs)
• The pool can be configured with a minimum number of idle instances to ensure fast startup
• This approach is more cost-effective than keeping clusters running 24/7 while providing similar startup benefits