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RAN-DBDEA-00195
Ranvier · DBDEA

Exam DBDEA Topics: ingestion Difficulty: medium

A Structured Streaming query reads a 12-partition Kafka topic that feeds a Delta table. During traffic spikes, single micro-batches grow enormous and overwhelm the cluster. The team wants to cap how much data each micro-batch consumes and asks what controls the number of concurrent read tasks. Which configuration answers both concerns?

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  1. A. Executor count: read parallelism depends only on how many workers the cluster has, and the topic's partition layout plays no role
  2. B. Topic repartitioning: changing the partition layout is the only throttling mechanism, because Spark exposes no rate-limiting option for Kafka
  3. C. Manual pacing: Kafka sources cannot be rate-limited in Spark at all, so an operator must pause and resume the query during spikes
  4. D. Offset cap: set kafka.maxOffsetsPerTrigger to bound each micro-batch; read tasks follow the topic's partitions, raised by minPartitions
Reveal answer

Correct answer: Offset cap: set kafka.maxOffsetsPerTrigger to bound each micro-batch; read tasks follow the topic's partitions, raised by minPartitions

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

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Official Answer (D): Offset cap: set kafka.maxOffsetsPerTrigger to bound each micro-batch; read tasks follow the topic's partitions, raised by minPartitions

Ranvier · DBDEA

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