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DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE · Question #12

A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table. The cade block used by the data engineer is…

The correct answer is D. trigger(processingTime="5 seconds"). To configure a Structured Streaming query to execute micro-batches every 5 seconds, the processingTime option within the trigger method should be used.

Submitted by skyler.x· Apr 18, 2026ELT with Spark SQL and Python

Question

A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table. The cade block used by the data engineer is below: If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

Exhibit

DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE question #12 exhibit

Options

  • Atrigger("5 seconds")
  • Btrigger()
  • Ctrigger(once="5 seconds")
  • Dtrigger(processingTime="5 seconds")
  • Etrigger(continuous="5 seconds")

How the community answered

(18 responses)
  • A
    6% (1)
  • D
    89% (16)
  • E
    6% (1)

Why each option

To configure a Structured Streaming query to execute micro-batches every 5 seconds, the `processingTime` option within the `trigger` method should be used.

Atrigger("5 seconds")

The syntax `trigger("5 seconds")` is not a valid way to configure a processing time trigger in Spark Structured Streaming.

Btrigger()

Calling `trigger()` without arguments defaults to `processingTime=0`, meaning the query will process micro-batches as fast as possible, not every 5 seconds.

Ctrigger(once="5 seconds")

The syntax `trigger(once="5 seconds")` is incorrect; `trigger(once=True)` is used for a single batch execution, not recurring micro-batches at an interval.

Dtrigger(processingTime="5 seconds")Correct

The `trigger(processingTime="5 seconds")` option specifies a fixed interval between micro-batch triggers, ensuring the query attempts to process data every 5 seconds, making it ideal for the described scenario.

Etrigger(continuous="5 seconds")

The `trigger(continuous="5 seconds")` option configures continuous processing mode, which is different from micro-batch processing at a fixed interval and is not widely used due to specific requirements and limitations.

Concept tested: Spark Structured Streaming triggers

Source: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers

Topics

#Structured Streaming#Triggers#Micro-batching#Spark Streaming Configuration

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