Develop a Stream Processing Solution DP-203 Practice Questions (Page 7)

Questions 31–35

1. 31foundation · easy

   In Spark Structured Streaming, which function is used to perform windowed aggregations on event-time columns?

   • Aagg()
   • Bover()
   • CgroupBy()
   • Dwindow()
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2. 32application · medium

   A media streaming company ingests viewer activity events into an Event Hub with 16 partitions. They use Azure Stream Analytics to aggregate events per minute and write results to Azure Blob Storage. The event rate varies significantly throughout the day, with peaks during prime time. They want to ensure the job can handle the peak load without dropping events, but they also want to minimize cost during off-peak hours. What should they do?

   • AReduce the number of Event Hub partitions to 4 to lower the ingestion cost.
   • BUse autoscaling to adjust the number of streaming units based on the input rate.
   • CProvision a fixed number of streaming units that can handle the peak load at all times.
   • DUse a tumbling window of 5 minutes instead of 1 minute to reduce the processing load.
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4. 33foundation · easy

   Which Azure Stream Analytics feature allows you to automatically adjust the number of streaming units based on workload?

   • AAutoscaling
   • BPartitioning
   • CWatermarking
   • DCheckpointing
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5. 34application · medium

   A data engineering team runs a Spark structured streaming job on Azure Databricks that reads from Event Hubs and writes aggregated results to a Delta Lake table. The job occasionally fails due to a transient network error. They need to ensure that after a restart, the job resumes from the last committed offset and does not reprocess the entire batch. What should they configure?

   • AConfigure the Event Hubs consumer group to use the same name as the Spark application.
   • BSet the startingOffsets option to 'earliest' in the readStream configuration.
   • CUse the awaitTermination method in the streaming query to automatically restart the job on failure.
   • DEnable checkpointing by setting the checkpointLocation option to a path in DBFS or Azure Data Lake Storage.
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6. 35application · medium

   A healthcare company uses Azure Stream Analytics to process patient vitals from Event Hubs and output to Azure Blob Storage. Due to a bug in the query, the output data for the last 3 hours is incorrect. The company has the raw data archived in Event Hubs (with a retention period of 7 days). They need to reprocess the affected data and correct the output. What should they do?

   • ARestart the existing Stream Analytics job with the corrected query and set the starting position to 'Last output event'.
   • BModify the existing job's query and use the 'When last stopped' starting position to resume from the last checkpoint.
   • CUse the Event Hubs 'Replay' feature to resend the events to the same job.
   • DCreate a new Stream Analytics job that reads from the Event Hubs archive (via Azure Blob Storage) and writes to a corrected output path.
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