Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer is designing a Delta Live Tables (DLT) pipeline. They need to ensure that records with missing values in the 'customer_id' column are dropped during the ingestion process. Which constraint syntax should be used?
⚠ Common exam trap
Candidates frequently confuse DLT expectations like 'expect_or_drop' with standard Spark SQL filter clauses or constraint keywords from traditional relational databases.
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
✓
@expect_or_drop(customer_id IS NOT NULL)
The EXPECT DROP VIOLATION constraint is a native DLT feature designed specifically for data quality enforcement. By applying this, the pipeline automatically discards rows that fail the specified predicate while allowing valid records to proceed. This approach is essential in production data engineering to maintain data integrity and prevent downstream errors caused by null values, ensuring only high-quality data enters the silver or gold tables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
@expect_or_fail(customer_id IS NOT NULL)
Why it's wrong here
This constraint halts the entire pipeline execution when a violation occurs. While it protects data quality, it is too restrictive for scenarios where individual row drops are acceptable. In high-volume production streams, stopping the pipeline is usually avoided unless the data breach is critical for downstream business logic.
- ✗
@expect(customer_id IS NOT NULL)
Why it's wrong here
The standard @expect constraint logs the violation as a metric in the pipeline's event log but does not remove the record from the dataset. The invalid row persists in the table, which fails to meet the requirement of dropping the records during the transformation and ingestion workflow.
- ✓
@expect_or_drop(customer_id IS NOT NULL)
Why this is correct
This specific DLT decorator instructs the pipeline to evaluate the expression and drop any rows that return false. It is the correct mechanism for filtering out invalid data silently while allowing the pipeline to continue processing subsequent batches of data without interruption or manual intervention.
- ✗
@filter(customer_id IS NOT NULL)
Why it's wrong here
The @filter decorator is not a standard DLT constraint syntax for row-level quality enforcement. While a standard SQL WHERE clause or PySpark filter could be used in a transformation, the DLT constraint syntax is specifically designed to integrate with the DLT observability and quality reporting features.
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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.