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Answer: Spark configuration settings defined via the Clusters UI apply to the compute level, meaning every notebook or job attached to that cluster inherits those settings.
### Explanation **Correct Answer: B** Spark configurations set through the **Clusters UI** or the **Clusters REST API** are applied at the compute level. This means every notebook, job, or pipeline attached to that cluster inherits those settings. Note that changes to a cluster's Spark configuration require a restart to take effect. **Why the other options are incorrect:** * **A is incorrect:** If a cluster is updated via the REST API while in a `RUNNING` state, it must be restarted for the new attributes to take effect. This restart interrupts all active notebooks and jobs. * **C is incorrect:** Changes made within a notebook (e.g., using `spark.conf.set`) are local to that specific notebook's `SparkSession`. They do not leak into or affect other notebooks sharing the same cluster. * **D is incorrect:** Databricks allows notebook-scoped settings to override cluster-level defaults. If you use `spark.conf.set` or `SET` in SQL, the notebook-level value takes precedence for the life of that session.
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When configuring Spark properties on the Databricks platform, which of the following statements regarding the scope and impact of those settings is accurate?
A
Modifying the Spark configuration of a running interactive cluster via the Databricks REST API allows the changes to take effect immediately without interrupting active workloads.
B
Spark configuration settings defined via the Clusters UI apply to the compute level, meaning every notebook or job attached to that cluster inherits those settings.
C
Any Spark configuration parameters defined within a specific notebook are automatically applied to all other Spark sessions sharing the same interactive cluster.
D
If a Spark configuration property is defined at both the cluster level and within a notebook session, the notebook-specific setting is ignored in favor of the cluster default.