Data Engineering Associate with Databricks Practice Exam

Which of the following represents a trigger parameter in Databricks?

1. Spark.table().trigger(availableNow=True)

2. DataStreamReader.load()

3. DataFrameWriter.save()

4. DataFrameReader.schema()

The correct answer is: Spark.table().trigger(availableNow=True)

The representation of a trigger parameter in Databricks is accurately reflected in the first choice. In the context of streaming data processing using Spark structured streaming, the trigger determines how often new data should be processed. The specific usage of `Spark.table().trigger(availableNow=True)` indicates that the stream will be processed based on available data points, allowing for the immediate processing of whatever data is currently available, rather than waiting for a certain interval or batch time. The other options do not relate to trigger parameters. DataStreamReader.load() is used for reading stream data but does not determine how often that data is processed. DataFrameWriter.save() is involved in writing DataFrames to storage but operates independently of streaming triggers. DataFrameReader.schema() is used to specify or infer the schema for reading data into a DataFrame; it does not pertain to the frequency or conditions under which data is processed, which is what a trigger parameter controls. Thus, the first choice accurately encapsulates the functionality and purpose of a trigger parameter in the Databricks environment.