Question 7

You are creating classic compute resources in a Unity Catalog-enabled Azure Databricks workspace and must apply the correct feature settings. The platform's requirements are: - A long-running **production job cluster** that runs operational Delta ETL and must avoid compatibility surprises and be thoroughly testable before upgrades. - A **data science cluster** that needs the prebuilt ML stack (TensorFlow, PyTorch, XGBoost) integrated with the workspace. - SQL and DataFrame transformations on the production job cluster should be accelerated by the built-in vectorized engine. You configure these via the create-compute UI and the Clusters API. Select the THREE statements that correctly describe how to apply these feature settings. **(Choose THREE.)**

• AFor the production job cluster running operational workloads, choose a Long Term Support (LTS) Databricks Runtime version so you avoid compatibility issues and can test before upgrading.
• BFor the data science cluster, select the **Machine learning** checkbox so the cluster uses Databricks Runtime ML, which preloads TensorFlow, PyTorch, and XGBoost.
• CTo accelerate SQL/DataFrame transformations, enable **Use Photon Acceleration**; when creating the cluster via the Clusters API you must explicitly set `runtime_engine` to `PHOTON`.
• DPhoton must be disabled on any cluster that uses a Databricks Runtime ML version because Photon and ML runtimes are mutually exclusive on all versions.
• ESelecting the **Machine learning** checkbox automatically sets the cluster's access mode to **Standard (shared)**.
• FChoosing an LTS Databricks Runtime version automatically disables Photon to guarantee long-term API stability.