Databricks-GenAI-Assoc Governance Practice Question
A data engineer needs to ensure that sensitive PII columns are masked for specific groups while remaining visible to analysts. Which Unity Catalog feature should be used to implement this requirement?
⚠ Common exam trap
Candidates often suggest creating multiple views or tables with filtered data. This is inefficient and prone to errors; dynamic data masking is the correct, centralized feature for this.
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
✓
Dynamic data masking
Unity Catalog dynamic data masking allows administrators to apply functions to columns that redact or obfuscate data based on the user's role or group membership. By using SQL functions like mask_email or custom UDFs within a masking policy, the data remains consistent at the physical layer while presenting transformed values at query time. This ensures compliance with privacy regulations without creating multiple copies of datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Row-level security filters
Why it's wrong here
Row-level security restricts access to specific records within a table based on user attributes. It does not modify column-level content or apply masking functions to sensitive data fields. While it controls visibility of rows, it does not address the need to mask specific columns while showing others.
- ✓
Dynamic data masking
Why this is correct
Dynamic data masking is specifically designed to redact or transform sensitive column data in real-time based on the user's identity or group. By attaching a masking function to a column in Unity Catalog, data engineers can ensure that analysts see masked results without modifying the underlying data files.
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Credential passthrough
Why it's wrong here
Credential passthrough is an older authentication mechanism that allows users to authenticate to Azure Data Lake Storage directly using their identity. It is not designed for data masking or granular column-level control within a Unity Catalog environment and is generally discouraged in favor of Unity Catalog access control.
- ✗
Attribute-based access control (ABAC)
Why it's wrong here
ABAC uses tags and metadata attributes to determine access levels to objects. While useful for broader policy management, it does not provide the specific masking functionality required to transform column values (like redacting emails) during query execution. Masking requires a specific SQL function-based policy applied to the table.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-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-GenAI-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-GenAI-Assoc exam.