Databricks Certified Data Engineer - Associate
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A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which action can the data engineer perform to improve the start up time for the clusters used for the Job?
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A
They can use endpoints available in Databricks SQL
B
They can use jobs clusters instead of all-purpose clusters
C
They can configure the clusters to autoscale for larger data sizes
D
They can use clusters that are from a cluster pool
Explanation:
Explanation
Correct Answer: D - They can use clusters that are from a cluster pool
Why this is correct:
- Cluster pools are pre-warmed, ready-to-use clusters that significantly reduce cluster startup time.
- When using cluster pools, Databricks maintains a pool of idle, ready instances that can be quickly assigned to jobs.
- This eliminates the time needed to provision and configure new clusters from scratch, which is the main cause of slow startup times.
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.
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B: They can use jobs clusters instead of all-purpose clusters - Partially relevant but not the best answer. While jobs clusters are optimized for production workloads, they still need to be provisioned each time unless they're from a pool.
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C: They can configure the clusters to autoscale for larger data sizes - Incorrect. Autoscaling helps with handling varying workloads but doesn't improve initial cluster startup time.
Key Concept: Cluster pools maintain warm instances that can be quickly assigned to jobs, reducing startup latency from minutes to seconds. This is particularly beneficial for nightly jobs where predictable performance is crucial.
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