Describe Apache Spark - Streaming Processing and Architecture DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which statement best describes the role of Spark Streaming within the Apache Spark ecosystem?

   • AIt is a separate cluster computing framework independent of Spark Core
   • BIt enables processing of live data streams using a micro-batch approach
   • CIt replaces the need for batch processing in Spark
   • DIt provides a library for machine learning on streaming data
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   How does Spark Streaming model streaming data for processing?

   • AAs a distributed queue that is consumed by multiple workers
   • BAs a continuous, unbounded table that is queried with SQL
   • CAs a single large RDD that is updated with new data
   • DAs a series of micro-batches, each represented by an RDD
   Reveal Solution Discussion Select an answer first
4. 18application · medium

   A telecommunications company is designing a real-time analytics pipeline to monitor network alarms. They want to use Apache Spark for both real-time processing and historical analysis. Which statement accurately describes the role of Spark Streaming in this context?

   • ASpark Streaming is only for batch processing of historical data.
   • BSpark Streaming ingests live data and processes it in micro-batches, while Spark Core handles the underlying execution.
   • CSpark Streaming requires a separate cluster from Spark Core.
   • DSpark Streaming replaces Spark Core for all data processing tasks.
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A company wants to process real-time IoT data and also run batch jobs on the same cluster. They want to minimize operational overhead. Which approach leverages Spark's unified engine?

   • AUse a different framework for streaming, such as Apache Flink.
   • BUse separate clusters for streaming and batch to avoid interference.
   • CUse Spark Streaming only for batch processing.
   • DUse Spark Streaming for real-time and Spark Core for batch on the same cluster.
   Reveal Solution Discussion Select an answer first
6. 20expert · hard

   A company has an existing Spark Streaming (DStreams) pipeline that processes sensor data. They want to add event-time based aggregations and exactly-once processing. They are considering migrating to Structured Streaming. What is the most significant challenge they will face?

   • AStructured Streaming does not support event-time processing.
   • BStructured Streaming cannot integrate with Kafka.
   • CStructured Streaming does not support checkpointing.
   • DRewriting the existing DStream transformations to the DataFrame API.
   Reveal Solution Discussion Select an answer first