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RAN-DBDEA-00031
Ranvier · DBDEA

Exam DBDEA Topics: ingestion Difficulty: medium

A junior engineer tries to start an incremental read of a Kafka topic called orders on a Confluent cluster: ```python df = (spark.read .format("confluent") .option("bootstrap", "broker1:9092") .option("topic", "orders") .load()) ``` The call fails immediately. Which correction correctly starts the streaming read so that the JSON message bodies can be parsed into a Delta table?

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  1. A. Use spark.readStream.format("kafka") with kafka.bootstrap.servers and subscribe set to "orders"; parse the binary columns downstream
  2. B. Keep spark.read and just change the format to "kafka"; batch reads automatically become incremental once the result is written to a Delta table
  3. C. Use spark.readStream.format("cloudFiles") with cloudFiles.format set to "kafka" so that Auto Loader discovers new messages as they arrive
  4. D. Use spark.readStream.format("jdbc") with the broker URL, because Kafka exposes a JDBC interface for external consumers
Reveal answer

Correct answer: Use spark.readStream.format("kafka") with kafka.bootstrap.servers and subscribe set to "orders"; parse the binary columns downstream

Reference: https://docs.databricks.com/aws/en/connect/streaming/kafka/

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Official Answer (A): Use spark.readStream.format("kafka") with kafka.bootstrap.servers and subscribe set to "orders"; parse the binary columns downstream

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