Databricks-DE-Pro Debugging and Deploying Practice Question
A data engineer is debugging a Databricks job that reads from a Delta table and writes to another Delta table. The job occasionally fails with 'ConcurrentAppendException'. The engineer wants to minimize failures while maintaining data correctness. Which approach should the engineer take?
⚠ Common exam trap
The trap here is thinking that scaling compute or changing file format solves a transactional concurrency conflict, when the fix lies in write isolation and retry behavior.
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
✓
Use optimistic concurrency control with retry logic and partition the target table to reduce conflicts
ConcurrentAppendException arises when multiple writers append to the same Delta table partition. Delta's optimistic concurrency control detects the conflict. Adding retry logic allows transient conflicts to resolve, and partitioning the target table by a key that separates writers reduces overlapping appends, preserving correctness while minimizing failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable Delta Lake transaction logging on the target table
Why it's wrong here
Disabling transaction logging is not a supported or safe operation for Delta tables and would break ACID guarantees. The ConcurrentAppendException is a symptom of Delta's concurrency control working as intended. The engineer should adjust write patterns and use retries rather than attempt to disable core Delta functionality.
- ✗
Increase the job cluster's autoscaling maximum worker count
Why it's wrong here
Adding workers increases parallelism but does not resolve write conflicts between concurrent transactions. ConcurrentAppendException is a concurrency control error, not a resource shortage. More workers could even increase the likelihood of concurrent writes. The engineer should address transaction isolation and write patterns instead.
- ✓
Use optimistic concurrency control with retry logic and partition the target table to reduce conflicts
Why this is correct
ConcurrentAppendException occurs when two writers attempt to add files to the same partition. Delta Lake uses optimistic concurrency; retrying the transaction and partitioning the target table to isolate writes reduces the chance of conflicting appends. This maintains correctness while improving success rates.
- ✗
Switch the target table to a Parquet table to avoid transaction conflicts
Why it's wrong here
Parquet tables do not provide ACID transactions or concurrency control. Switching would remove the conflict error but also eliminate data correctness guarantees, potentially causing data corruption or inconsistent reads. The engineer should retain Delta Lake and address the concurrency issue properly rather than downgrade the storage format.
Visual reference
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.