hardMultiple SelectObjective-mapped
DP-203 Practice Question: A company uses Azure Databricks to process…
A company uses Azure Databricks to process streaming data from Event Hubs. The data is written to a Delta table. The job occasionally fails due to checkpoint corruption. Which THREE measures should you implement to improve reliability?
⚠ Common exam trap
A common mix-up: candidates confuse scaling solutions (increasing cluster size or batch interval) with reliability mechanisms, failing to recognize that checkpoint durability and data loss tolerance are the core mitigations for corruption and streaming failures.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure checkpointing to a durable storage like Azure Data Lake Storage.
Checkpointing to durable storage like Azure Data Lake Storage (ADLS) ensures that streaming progress metadata is persisted across cluster restarts and failures. ADLS provides high durability and availability, preventing checkpoint corruption that can occur with local or ephemeral storage, thereby enabling exactly-once processing guarantees in Structured Streaming.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure checkpointing to a durable storage like Azure Data Lake Storage.
Why this is correct
Durable storage prevents corruption.
- ✗
Increase the batch interval to reduce load.
Why it's wrong here
May reduce throughput, not reliability.
- ✗
Increase the cluster size to handle spikes.
Why it's wrong here
Does not address checkpoint corruption.
- ✓
Use Structured Streaming with `failOnDataLoss` set to false.
Why this is correct
Prevents job failure on data loss.
- ✓
Implement a retry policy with exponential backoff for transient failures.
Why this is correct
Handles transient issues.
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