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DP-750 · 1.1.1 · Set up and configure an Azure Databricks environment

Choose compute type

Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute

Match compute to workload shape, isolation needs, and who should manage the infrastructure.

Choose by workload

Infographic comparing Azure Databricks compute choices for interactive notebooks, automated jobs, and SQL analytics.

Start with the workload. Serverless is the normal recommendation for supported interactive and automated workloads. SQL analytics belongs on a SQL warehouse. Use classic compute when the workload needs configuration or capabilities that serverless does not provide.

Job tasks choose compute separately

  • Notebook or Python taskServerless jobs
  • SQL taskSQL warehouse
  • Custom classic settings are requiredClassic jobs compute
Compute for each task

A Lakeflow Job selects compute per task rather than forcing one compute type on the whole workflow.

Standard or dedicated

  • Standard

    Users
    Multiple isolated users
    Use first
    Yes

  • Dedicated

    Users
    One user or group
    Use first
    Only for specialized requirements
Interactive access mode

Standard is the normal shared choice. Dedicated is for workloads that need capabilities such as GPU or other specialized requirements.

Key points

  • Serverless: Databricks manages infrastructure for supported workloads
  • Jobs compute: Use job-oriented compute for automated tasks
  • SQL warehouse: Optimized compute for SQL analytics and BI
  • All-purpose: Interactive notebook development and exploration

Exam trap

Do not choose interactive all-purpose compute by default for scheduled production jobs.

Sources · 4 Microsoft Learn pages Show ▾

Checked on October 4, 2026. Report a problem

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