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 of the following actions 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 be single-node
D
They can use clusters that are from a cluster pool
E
They can configure the clusters to autoscale for larger data sizes
Explanation:
Correct Answer: D
Explanation:
Cluster pools are specifically designed to reduce cluster startup time by pre-provisioning and maintaining a pool of idle, ready-to-use instances. When a job needs a cluster, it can quickly acquire one from the pool rather than waiting for new instances to be provisioned from scratch.
Analysis of other options:
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A: They can use endpoints available in Databricks SQL - Databricks SQL endpoints are for SQL analytics workloads, not for general Spark jobs, and they don't directly address cluster startup time for job tasks.
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B: They can use jobs clusters instead of all-purpose clusters - While jobs clusters are optimized for job execution, they still need to be provisioned from scratch unless they're part of a cluster pool. Jobs clusters alone don't guarantee faster startup times.
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C: They can configure the clusters to be single-node - Single-node clusters might start slightly faster due to simpler configuration, but the main bottleneck in cluster startup is instance provisioning, not cluster size. Additionally, single-node clusters may not have sufficient resources for the job's tasks.
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E: They can configure the clusters to autoscale for larger data sizes - Autoscaling helps with resource optimization during job execution but doesn't address initial cluster startup time. In fact, autoscaling might add overhead as the cluster needs to monitor workload and scale accordingly.
Best Practice: Using cluster pools is a recommended approach for jobs that run regularly (like nightly jobs) because:
- Pre-warmed instances are ready to use
- Reduces cold start time significantly
- More cost-effective than keeping clusters running 24/7
- Better resource utilization through pooling
According to Databricks documentation, cluster pools can reduce cluster startup times by up to 50-75% compared to creating clusters from scratch.
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