Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer wants to use 'Expectations' in Delta Live Tables to monitor data quality. What happens if a record violates an expectation defined with the 'fail' constraint?
⚠ Common exam trap
Candidates often assume that 'fail' just marks a record as invalid or sends it to a quarantine table, forgetting that it is a hard stop for the entire pipeline.
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
✓
The pipeline execution stops immediately, and the batch is not committed.
In DLT, the 'fail' expectation is a strict data quality gate. If any record in a micro-batch fails the validation, the entire pipeline will stop processing, and the current batch will fail. This prevents bad data from reaching the target, which is essential for pipelines where data integrity is the highest priority. It forces the developer to address the underlying data quality issues before the pipeline can continue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The record is automatically moved to a side table for manual review.
Why it's wrong here
The 'fail' constraint does not automatically perform any quarantine logic. It simply stops the pipeline execution. If you need to save records for later review, you would need to use a different constraint type or implement separate logic within your transformation to filter and store invalid records explicitly.
- ✓
The pipeline execution stops immediately, and the batch is not committed.
Why this is correct
The 'fail' constraint is designed to act as a hard stop. By stopping the pipeline, DLT ensures that no invalid records are written to the target table. This behavior is crucial for enforcing strict data quality standards and ensuring that downstream users do not consume corrupt data.
- ✗
The record is dropped, and the pipeline continues to process the next record.
Why it's wrong here
This behavior would be characteristic of a 'drop' or 'warn' expectation, not a 'fail' expectation. The 'fail' constraint is, by definition, intended to stop the pipeline, not to silently ignore invalid data. Dropping data silently can lead to missing metrics that stakeholders may not notice until it is too late.
- ✗
The error is logged, and the pipeline continues to process the batch.
Why it's wrong here
Logging without failing is the behavior of the 'warn' expectation. The 'fail' constraint is explicitly designed to stop processing. If the pipeline continued, it would violate the intent of the 'fail' operator, which is to guarantee that the data reaching the destination meets all quality criteria without exception.
About these practice questions
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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-Assoc 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-Assoc exam.