Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
A Data Engineer needs to enforce a NOT NULL constraint on a specific column in a Delta table while maintaining the ability to perform high-performance streaming writes. Which approach is the most efficient and native method to ensure this data quality requirement?
⚠ Common exam trap
Candidates often try to implement data quality checks using external post-processing scripts or complex notebook logic rather than utilizing native table constraints.
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
✓
Add a CHECK constraint to the table using the ALTER TABLE ADD CONSTRAINT command.
Using Delta Lake's CHECK constraints is the most efficient way to enforce data quality at write time. By defining a constraint, the Delta engine validates every incoming row against the business logic before committing the transaction. This avoids post-process cleanup jobs, reduces storage waste, and ensures that downstream consumers always receive valid data, which is critical for maintaining robust data pipelines in a Lakehouse architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a filter transformation in the DataFrame after the data is written to the table.
Why it's wrong here
Applying a filter post-write does not prevent invalid data from entering the table. It only processes rows already committed, meaning the table remains corrupted until the filter executes. This approach fails to provide strict schema enforcement and does not meet the requirement of enforcing constraints during the write operation.
- ✗
Use an external Delta Live Tables expectation to quarantine bad records.
Why it's wrong here
While quarantine is useful for data cleansing, the question specifically asks for an enforcement mechanism on the table. DLT expectations are declarative, but native Delta constraints provide a lower-latency, built-in mechanism for schema enforcement that is independent of the DLT pipeline framework and applies directly to the underlying table storage.
- ✓
Add a CHECK constraint to the table using the ALTER TABLE ADD CONSTRAINT command.
Why this is correct
The ALTER TABLE ADD CONSTRAINT command natively integrates with Delta Lake's transaction log to enforce validation at the moment of ingestion. It is highly performant and ensures that no transaction containing a null value in the specified column will be committed, effectively preventing data quality issues at the source.
- ✗
Perform a manual check within the Spark Structured Streaming loop before appending to the sink.
Why it's wrong here
Performing manual checks inside a loop adds significant latency and code complexity. It requires maintaining custom logic that is prone to errors and harder to maintain as the schema evolves. Native SQL constraints are optimized by the Spark engine and are significantly more reliable than custom application-level programmatic validations.
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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.