Databricks Certified Data Engineer - Associate

Explanation:

When working with streaming data in Databricks, you need to use spark.readStream instead of spark.read to create a streaming DataFrame. The key differences are:

1. spark.readStream creates a streaming DataFrame that continuously reads data as it arrives, while spark.read creates a static DataFrame that reads all data at once.

2. The original code uses batch processing (spark.read), which is suitable for one-time data ingestion but not for streaming sources.

3. The other options are incorrect:

   • A: There's no predict function in the code, so this option doesn't apply.
   • B: While maxFilesPerTrigger is a valid streaming option, replacing schema(schema) with it would remove the schema definition, which is often necessary.
   • C: You can read from a table name directly in streaming mode; you don't need to use the path location.
   • D: format("stream") is not a valid format; you should keep format("delta") for Delta tables.

4. After replacing spark.read with spark.readStream, the code would look like:

transactions_df = (spark.readStream
    .schema(schema)
    .format("delta")
    .table("transactions"))

transactions_df = (spark.readStream
    .schema(schema)
    .format("delta")
    .table("transactions"))

This creates a streaming DataFrame that can process data incrementally as it arrives in the Delta table.