Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
You are developing a Delta Live Tables (DLT) pipeline and need to ensure high data quality. Which TWO of the following statements correctly describe how Expectations work within DLT?
⚠ Common exam trap
Candidates often assume DLT expectations can only be written in Python or that violations automatically drop tables, missing the flexibility of SQL and 'expect_or_fail'.
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
✓
You can use 'expect_or_fail' to stop the pipeline if a constraint is violated.
Expectations are a fundamental feature in Delta Live Tables that allow engineers to define data quality constraints directly within the DLT pipeline code. By specifying these rules, developers can monitor data health, quarantine corrupt rows, or fail pipelines when critical thresholds are breached. This declarative approach integrates governance into the data engineering workflow, ensuring that downstream consumers receive only validated, high-quality data while maintaining robust audit trails for compliance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Expectations only support dropping rows that fail validation.
Why it's wrong here
DLT expectations offer flexible handling of invalid data. You can choose to drop rows, fail the pipeline, or simply record the constraint violation in the event logs without taking action. Limiting expectations strictly to dropping rows ignores the 'expect_or_fail' and 'expect_or_drop' options available for nuanced data quality management.
- ✓
You can use 'expect_or_fail' to stop the pipeline if a constraint is violated.
Why this is correct
The 'expect_or_fail' operator is specifically designed for mission-critical data. If a single row violates the defined expectation, the pipeline execution is halted immediately. This ensures that no downstream transformations are processed with potentially tainted data, maintaining strict integrity requirements for sensitive analytical or operational datasets.
- ✓
Expectations can be defined as Python decorators or SQL constraints.
Why this is correct
Delta Live Tables supports expectations through both Python decorators in standard DLT libraries and SQL 'CONSTRAINT' syntax within DLT SQL views. This versatility allows teams to standardize their data quality framework regardless of the language used for transformation logic, ensuring consistency across different types of pipeline implementations.
- ✗
Expectations must be configured in the pipeline settings JSON file.
Why it's wrong here
Expectations are defined within the logic of the pipeline transformation code itself, not in the pipeline settings JSON. By embedding these rules within the notebook or SQL file, developers maintain tight coupling between the data structure and its associated quality standards, promoting better readability and simplified maintenance of pipelines.
- ✗
Expectations automatically fix invalid data values.
Why it's wrong here
Expectations are validation tools, not transformation tools. They evaluate the data against defined predicates to report or handle quality issues; they do not perform data cleansing or imputation. Fixing invalid data requires explicit transformation logic using functions like 'when' or 'coalesce' before or during the application of expectations.
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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.