Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer is designing a Delta Lake Bronze-to-Silver pipeline in Databricks and needs to ensure that downstream consumers receive high-quality data. Which TWO data quality enforcement mechanisms are natively supported in Delta Live Tables using expectations?
⚠ Common exam trap
Candidates frequently mistake 'DROP ROW' for 'FAIL UPDATE' or vice versa, failing to distinguish between the behavior of discarding individual bad records versus halting the entire pipeline execution.
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
✓
CONSTRAINT expectation_name EXPECT (column_name IS NOT NULL) ON VIOLATION DROP ROW
Delta Live Tables provides native expectation syntax to monitor and enforce data quality constraints directly within pipeline definitions. Data engineers can configure expectations to either drop invalid records or fail the pipeline execution when violations occur, ensuring robust governance. These declarative validation rules are critical for maintaining trusted data assets across modern enterprise Medallion architectures without writing complex custom validation code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
CONSTRAINT expectation_name EXPECT (column_name IS NOT NULL) ON VIOLATION DROP ROW
Why this is correct
This declarative expectation syntax successfully instructs Delta Live Tables to validate the specified condition and automatically drop any incoming records that violate the constraint, ensuring only clean data persists in the target table.
- ✓
CONSTRAINT expectation_name EXPECT (column_name IS NOT NULL) ON VIOLATION FAIL UPDATE
Why this is correct
This native expectation clause correctly tells the Delta Live Table pipeline to immediately halt execution and fail the update if any incoming record fails the validation rule, preventing corrupted or incomplete datasets from propagating.
- ✗
ASSERT expectation_name ON VIOLATION RETRY
Why it's wrong here
Delta Live Tables does not support an ASSERT clause with a retry violation action; expectations must use either drop row, fail update, or warning actions to handle data quality constraint violations during pipeline execution.
- ✗
FILTER expectation_name WHERE column_name IS NOT NULL
Why it's wrong here
The FILTER clause is a standard SQL filtering construct rather than a native Delta Live Tables expectation mechanism, and it lacks the built-in violation handling actions required for automated data quality monitoring.
- ✗
VALIDATE expectation_name ON ERROR IGNORE
Why it's wrong here
The VALIDATE keyword is not part of the Delta Live Tables expectation syntax, and ignoring errors is handled by omitting violation clauses rather than using an explicit validate statement with an ignore action.
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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.