AI Associate Data for AI Practice Question
A data pipeline fails intermittently when processing large CSV files. The error log shows 'OutOfMemoryError'. Which configuration change is most likely to resolve this?
⚠ Common exam trap
Salesforce often tests the misconception that increasing parallelism (worker threads) solves memory issues, but in reality, more threads increase memory pressure and can trigger OutOfMemoryError faster.
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
✓
Increase the heap memory for the processing application.
The OutOfMemoryError indicates that the Java Virtual Machine (JVM) heap space is exhausted while processing large CSV files. Increasing the heap memory (e.g., using -Xmx flag) allocates more memory to the application, allowing it to handle larger datasets without crashing. This directly addresses the root cause of insufficient memory for the data pipeline's processing workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a smaller file size limit.
Why it's wrong here
While it might avoid the error, it doesn't solve the underlying memory problem and could lead to data loss.
- ✗
Increase the number of worker threads.
Why it's wrong here
More threads increase memory contention and may worsen the issue.
- ✗
Switch to XML format.
Why it's wrong here
XML is more verbose and would increase memory usage, not decrease.
- ✓
Increase the heap memory for the processing application.
Why this is correct
Increasing heap memory provides more space for large file processing.
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