Explanation
The answer is trigger(processingTime = "15 Minutes")
Triggers:
*Unspecified
This is the default. This is equivalent to using processingTime="500ms"
*Fixed interval micro-batches .trigger(processingTime="2 minutes")
The query will be executed in micro-batches and kicked off at the user-specified intervals
*One-time micro-batch .trigger(once=True)
The query will execute a single micro-batch to process all the available data and then stop on its own
*One-time micro-batch.trigger .trigger(availableNow=True) -- New feature a better version of (once=True) Databricks supports trigger(availableNow=True) in Databricks Runtime 10.2 and above for Delta Lake and Auto Loader sources. This functionality combines the batch processing approach of trigger once with the ability to configure batch size, resulting in multiple parallelized batches that give greater control for right-sizing batches and the resultant files.

Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html

Explanation
The Answer is DELETE and UPDATE permissions do not exit, you have to use MODIFY which provides both Update and Delete permissions.
Please note: TABLE ACL privilege types are different from Unity Catalog privilege types, please read the question carefully.
Here is the list of all privileges in Unity Catalog:

Unity Catalog Privileges
https://learn.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/language-manual/sql-ref-privileges#priv Table ACL privileges
https://learn.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#privileges

Explanation
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.