Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
A data engineer is using Delta Live Tables (DLT) to build a pipeline that ingests data from a streaming source. The pipeline must ensure that the target table is updated incrementally and that data quality constraints are enforced. The engineer wants to use expectations to drop invalid records while maintaining pipeline performance. Which TWO of the following are true regarding DLT expectations and their behavior? (Choose two.)
⚠ Common exam trap
The trap here is assuming that 'drop' causes a pipeline failure or that expectations are only checked once.
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
✓
Expectations with the 'drop' action will remove records that violate the constraint from the target table.
DLT expectations support actions like warn, drop, and fail. The 'drop' action removes invalid records and continues, while 'fail' stops the pipeline. These actions apply to both streaming tables and materialized views, and are evaluated on every update, not just the initial load. Thus, the statements about 'drop' and 'fail' are correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
When an expectation with 'drop' action is violated, the pipeline fails and must be restarted.
Why it's wrong here
The 'drop' action does not fail the pipeline; it simply drops the violating records and continues processing. The pipeline remains running, and the dropped records are recorded in the event log. Failing the pipeline is the behavior of the 'fail' action, not 'drop'.
- ✗
Expectations can only be applied to streaming tables, not to materialized views.
Why it's wrong here
Expectations can be applied to both streaming tables and materialized views in DLT. They are defined in the table definition regardless of the table type. This allows data quality checks for both streaming and batch pipelines. The constraint is on the data, not the table type.
- ✓
Expectations with the 'drop' action will remove records that violate the constraint from the target table.
Why this is correct
In DLT, expectations can be configured with different actions: warn, drop, or fail. The 'drop' action filters out records that violate the expectation, preventing them from being written to the target table. This is useful for maintaining data quality without failing the pipeline. The dropped records are logged in the event log for monitoring.
- ✗
Expectations are evaluated only during the initial data load, not on subsequent incremental updates.
Why it's wrong here
Expectations are evaluated on every update, including incremental updates. For streaming tables, each micro-batch is checked against the expectations. This ensures continuous data quality enforcement. They are not limited to the initial load; they apply to all data processed by the pipeline.
- ✓
Expectations with the 'fail' action will immediately stop the pipeline and require manual intervention to resume.
Why this is correct
The 'fail' action causes the pipeline to fail immediately when a record violates the expectation. This stops processing and requires manual intervention to fix the data or adjust the expectation. It is typically used for critical data quality checks where invalid data should not be processed further.
About these practice questions
One of 267 original Databricks-DE-Pro practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.