Data Engineering Associate with Databricks Practice Exam

How do triggers operate in Structured Streaming?

1. They define schema evolution

2. They specify data retention periods

3. They define time windows for data processing

4. They set data ingestion limits

The correct answer is: They define time windows for data processing

Triggers in Structured Streaming are essential for controlling the frequency and timing of data processing. When using a trigger, users can specify how often the system is going to process the data that has been ingested. This allows for greater flexibility in handling streams of data, as you can choose to process data in real-time, at specific intervals, or based on other time-based criteria. Using triggers effectively means that you can optimize resource usage and manage the flow of data through your pipelines. For example, setting a trigger to process data every minute allows the system to batch incoming data for that duration and then execute processing tasks, making it easier to handle varying loads or to react in a timely manner to incoming information. The other options pertain to different aspects of data processing or management in streaming environments but do not accurately represent the function of triggers. For instance, schema evolution focuses on how the system handles changes to data structure over time, data retention periods cover how long to keep the data, and data ingestion limits refer to controlling the amount of data that the system can ingest at a given time. None of these options capture the time-based control that triggers provide in the structured streaming ecosystem.