Microsoft Fabric Data Engineer Associate DP-700 Practice Question
In a Microsoft Fabric notebook, you develop a PySpark Structured Streaming query that reads JSON events from Azure Event Hubs and writes five-minute order totals to a Delta table located in the Lakehouse's bronze zone. The solution must resume automatically with no data loss if the notebook runtime is restarted. When you configure the writeStream operation, which option must you set to meet the requirement?
Set option("ignoreChanges", "true") on the writeStream sink.
Set option("maxFilesPerTrigger", "1") on the writeStream sink.
Set option("checkpointLocation", "abfss://lakehouse/checkpoints/orders") on the writeStream sink.
Set option("mergeSchema", "true") on the writeStream sink.
Spark Structured Streaming keeps track of processed offsets and any intermediate aggregation state in a persistent checkpoint directory. By adding the checkpointLocation option to the writeStream definition, the query writes its metadata and state to reliable storage so that, after any failure or restart, Spark can recover exactly where it left off and continue processing without data loss or duplication. The other options serve different purposes: mergeSchema controls schema evolution when writing to Delta, ignoreChanges is used for Change Data Feed reads, and maxFilesPerTrigger simply limits the number of source files per micro-batch. None of those settings enable recovery after a failure.
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What is checkpointLocation in Spark Structured Streaming?
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How does Spark Structured Streaming ensure recovery after a restart or failure?
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Why doesn't mergeSchema, ignoreChanges, or maxFilesPerTrigger enable recovery?
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Microsoft Fabric Data Engineer Associate DP-700
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