Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
A Data Engineer needs to verify that the column 'user_id' is unique in a critical Gold table. What is the most efficient, non-blocking way to perform this check in a production environment?
⚠ Common exam trap
Candidates often suggest running a 'SELECT COUNT(DISTINCT id)' query as a post-process check. This is inefficient and reactive, whereas Delta constraints are proactive and enforced at the write level.
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
✓
Define a primary key constraint in the Delta table definition.
Using a Delta constraint or a DLT expectation is the most efficient way to enforce uniqueness at the storage layer. Unlike a full table scan query, which is reactive and performance-intensive, native constraints are checked during the commit process, providing proactive quality assurance. This ensures that the table never enters an invalid state, which is vital for the integrity of downstream machine learning models and reporting applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run a daily query: SELECT COUNT(user_id) - COUNT(DISTINCT user_id).
Why it's wrong here
This is a reactive approach that does not prevent duplicates from entering the table. It only detects them after the fact. Running this query daily is inefficient for large datasets because it requires a full table scan and does not guarantee the integrity of data in real-time.
- ✓
Define a primary key constraint in the Delta table definition.
Why this is correct
Delta Lake supports primary key constraints, which are natively enforced during the transaction. This is the most efficient and robust way to guarantee uniqueness, as the engine rejects any attempt to insert a duplicate value, maintaining the 'single source of truth' integrity required for production-grade analytical and operational data.
- ✗
Use a custom UDF to check for duplicates inside a Spark streaming loop.
Why it's wrong here
Custom UDFs are significantly slower than native SQL constraints because they are not optimized by the Spark engine and add significant overhead to the streaming pipeline. It is much better to leverage the built-in, highly optimized Delta constraint system for this kind of validation.
- ✗
Perform a left-anti join with the previous day's data every time the pipeline runs.
Why it's wrong here
This only checks for uniqueness compared to the previous batch, not globally across the entire table. It is complex to implement and does not guarantee global uniqueness, making it an ineffective solution for ensuring that every 'user_id' in the entire table remains unique over time.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.