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DP-750 · 1.1.3 · Set up and configure an Azure Databricks environment

Configure compute features

Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning

Select runtime capabilities such as Photon, Spark runtime, and ML support to match the workload.

Match features to the workload

Infographic explaining Databricks Runtime, Photon, runtime upgrades, and ML runtime choices.

Runtime choice determines Spark and bundled platform compatibility. Photon accelerates supported SQL and DataFrame operations. Databricks Runtime ML bundles machine-learning libraries for workloads that actually need them.

What the UI exposes

Microsoft Learn screenshot of Azure Databricks compute performance settings including runtime, Photon, worker type, and autoscaling.

Photon and runtime are explicit compute settings. Treat them as workload decisions rather than universal performance switches.

Upgrade deliberately

  1. 1Choose candidate runtimePrefer supported or LTS where appropriate
  2. 2Validate codeCheck Spark behavior
  3. 3Validate librariesCheck bundled and custom dependencies
  4. 4PromoteMove only after representative testing
Runtime upgrade

A newer runtime can change behavior and dependency versions. Validate before production rollout.

Key points

  • Photon: Native vectorized engine for supported SQL and DataFrame workloads
  • Runtime: Sets Spark and platform feature compatibility
  • ML runtime: Bundles libraries for machine learning workloads
  • Compatibility: Validate libraries and features before runtime upgrades

Exam trap

Do not select a machine learning runtime just because the workload uses Python.

Sources · 2 Microsoft Learn pages Show ▾

Checked on October 4, 2026. Report a problem

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