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RAN-DBDEA-00004
Ranvier · DBDEA

Exam DBDEA Topics: platform Difficulty: medium

A two-person data engineering team schedules a nightly Lakeflow Job that runs a PySpark notebook to refresh reporting tables. The team has no cluster administration expertise and wants to avoid selecting instance types, worker counts, or runtime versions. They also want the job's compute to become available quickly, with no infrastructure for them to deploy or configure. Which compute option best fits these requirements?

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  1. A. Serverless compute for jobs, which Databricks provisions, scales, and manages automatically
  2. B. A classic job cluster with manually selected worker instance types and autoscaling bounds
  3. C. A shared all-purpose cluster that remains running so the job can attach to it at night
  4. D. A classic job cluster that the team resizes each evening based on the previous run
Reveal answer

Correct answer: Serverless compute for jobs, which Databricks provisions, scales, and manages automatically

Reference: https://docs.databricks.com/aws/en/jobs/compute

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✦ Consolidated Synthesis (Wiki) rev. #1 (baseline) · CC BY-SA 4.0
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Official Answer (A): Serverless compute for jobs, which Databricks provisions, scales, and manages automatically

⚓ Official Documentation Anchor: https://docs.databricks.com/aws/en/jobs/compute
Ranvier · DBDEA

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