Develop a Stream Processing Solution DP-203 Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   In Spark Structured Streaming, what is the primary way to scale out processing across a cluster?

   • AEnabling watermarking
   • BUsing a single large executor
   • CIncreasing the number of executor cores and memory
   • DIncreasing the checkpoint interval
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2. 12application · medium

   A logistics company sends delivery status events to an Event Hub. The event payload has been stable for months, but the operations team recently added a new field 'temperature' to some events. The Stream Analytics job that processes these events fails when it encounters the new field. The company wants the job to continue processing and to include the new field in the output when present. What should they configure in the Stream Analytics job?

   • AAdd a second input to the job that reads the same Event Hub with a different schema that includes the temperature field.
   • BUse the GetRecordPropertyValue function to dynamically read the 'temperature' field and output it when it exists.
   • CChange the job's compatibility level to 1.0 to automatically handle schema changes.
   • DSet the event serialization format to JSON and configure the job to use 'Field order' with 'Ignore' for unknown fields.
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4. 13foundation · easy

   What is the primary purpose of an Azure Stream Analytics job?

   • ATo process and analyze streaming data in real time
   • BTo orchestrate data pipelines between on-premises and cloud
   • CTo provide a relational database for transactional workloads
   • DTo store large volumes of batch data for historical analysis
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5. 14application · medium

   A financial services company processes stock trade events from an Event Hub. Each event contains a trade timestamp, a stock symbol, and a price. They need to calculate the volume-weighted average price (VWAP) for each symbol over the last 10 minutes, updating the result as new trades arrive. The output must be stored in a Delta Lake table for downstream analytics. What should they use in Spark structured streaming?

   • AA sliding window of 10 minutes with watermarking on the trade timestamp, and then upsert the result into the Delta table using a merge operation.
   • BA global window with no watermark, and overwrite the Delta table with each micro-batch.
   • CA hopping window of 10 minutes with a hop of 5 minutes, and write only the latest window to the Delta table.
   • DA tumbling window of 10 minutes with watermarking, and append the results to the Delta table.
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6. 15foundation · easy

   What is Delta Lake primarily used for in stream processing?

   • ACompressing data to reduce storage costs
   • BEncrypting data at rest
   • CProviding ACID transactions and time travel on data lakes
   • DStoring streaming data in memory
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