Exam DP-750 Topic 1 Question 37 Discussion
Actual exam question for Microsoft's DP-750 exam
Question #: 37
Topic #: 1
Question #: 37
Topic #: 1
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data.
You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?
You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data.
You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?
Suggested Answer: A Vote an answer
To prevent your Apache Spark Structured Streaming job from reprocessing previously ingested data after a cluster restart, you must configure a streaming checkpoint directory.
Core Solution
Enable Checkpointing: Define the checkpointLocation option in your streaming write configuration.
Track Progress: Spark uses this directory to save the exact offset ranges of processed data.
Automatic Recovery: Upon restart, the engine reads the checkpoint and resumes precisely where it left off.
Implementation Example in python
# Configure the streaming write with a checkpoint path
(df.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation",
"/Volumes/catalog/schema/volume_name/checkpoints/job_name")
.toTable("catalog.schema.target_table"))
Reference:
https://medium.com/@salah.uddin_75300/architecture-of-a-streaming-machine-learning-data- pipeline-042200c8e7ff
Core Solution
Enable Checkpointing: Define the checkpointLocation option in your streaming write configuration.
Track Progress: Spark uses this directory to save the exact offset ranges of processed data.
Automatic Recovery: Upon restart, the engine reads the checkpoint and resumes precisely where it left off.
Implementation Example in python
# Configure the streaming write with a checkpoint path
(df.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation",
"/Volumes/catalog/schema/volume_name/checkpoints/job_name")
.toTable("catalog.schema.target_table"))
Reference:
https://medium.com/@salah.uddin_75300/architecture-of-a-streaming-machine-learning-data- pipeline-042200c8e7ff
by Leo at Sep 12, 2026, 12:01 AM
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