Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst needs to ensure that sensitive information in a table is not visible to unauthorized users. Which Unity Catalog feature is the most efficient way to achieve this at the row level?
⚠ Common exam trap
Candidates often suggest creating separate filtered views or duplicate tables for different user groups, which violates governance best practices and creates maintenance overhead.
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
✓
Apply Row Filters and Column Masks in Unity Catalog.
Row-level security (RLS) and column-level security are fundamental features of Unity Catalog that enable fine-grained access control. By applying these policies, organizations can ensure compliance with privacy regulations while maintaining data usability. Understanding how to define these filters allows analysts to secure data effectively without creating multiple copies of the same dataset for different user groups, which simplifies maintenance and ensures consistency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a separate view for each user group.
Why it's wrong here
Creating multiple views leads to view proliferation and makes it difficult to maintain and audit security policies. It is an outdated, inefficient practice that is prone to human error. Unity Catalog provides native row-level security functions that are more scalable, manageable, and secure than managing custom views for every user.
- ✓
Apply Row Filters and Column Masks in Unity Catalog.
Why this is correct
Row filters and column masks are built-in Unity Catalog features that enforce fine-grained access control dynamically. They allow administrators to define security rules that automatically filter rows or mask sensitive column values based on the user's role or attributes, providing a robust, scalable, and centralized method for protecting sensitive data assets.
- ✗
Use hard-coded SQL 'WHERE' clauses in every notebook.
Why it's wrong here
Relying on developers to remember to include security filters in their code is highly insecure and prone to failure. If a user forgets to add the clause, sensitive data is exposed. Security must be enforced at the platform level by the administrator, not left to individual developers to implement in their notebooks.
- ✗
Move sensitive data to a completely separate, private workspace.
Why it's wrong here
Isolating data into a separate workspace is an extreme and unnecessary measure that disrupts collaboration and data integration. It increases administrative overhead and creates data silos. Unity Catalog's fine-grained security policies are designed to handle this requirement within a single, unified environment, making it the preferred approach for modern data governance.
About these practice questions
One of 291 original Databricks-DA-Assoc 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-DA-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-DA-Assoc exam.