Databricks-GenAI-Assoc Data Preparation Practice Question
A Data Engineer needs to ensure that PII data in a Delta table is masked before serving it to non-privileged users. Which Databricks feature provides the most efficient, centralized control for this requirement?
⚠ Common exam trap
Candidates often suggest creating separate tables or filtering via application code. Unity Catalog masking is the centralized, efficient way to handle this without duplicating data or creating security gaps.
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
✓
Utilize Unity Catalog column-level masking functions.
Unity Catalog's dynamic views and column-level masking policies are the standard for securing PII. By applying functions like MASK or current_user() within a view definition, administrators decouple security logic from physical table storage. This approach is essential for compliance, ensuring that sensitive data is hidden at query time without duplicating datasets, thus maintaining a single source of truth while enforcing granular access control across all Databricks workspaces and compute resources.
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 Row-Level Security filters using Delta table constraints.
Why it's wrong here
Row-level security filters restrict access to specific records based on predicates, but they do not perform column-level masking. While useful for partitioning data access by region or department, they fail to hide sensitive fields like email or social security numbers from authorized rows in the table.
- ✗
Create materialized views with hard-coded redacted values.
Why it's wrong here
Materialized views create persistent copies of transformed data. Hard-coding redactions forces storage of redundant, masked data, which increases storage costs and management overhead. Dynamic masking is preferred because it applies logic at runtime, ensuring the underlying data remains accessible to privileged users without creating extra copies.
- ✓
Utilize Unity Catalog column-level masking functions.
Why this is correct
Unity Catalog enables defining dynamic masks on columns using SQL functions. This centralizes security policy enforcement, applying masking rules consistently across all users and compute clusters. It is the most robust method for PII protection because it prevents unauthorized visibility without altering the underlying raw data storage files.
- ✗
Implement Spark UDFs to perform in-memory data masking.
Why it's wrong here
Spark UDFs are executed within the compute cluster, which can lead to performance degradation and inconsistent enforcement. Furthermore, users can potentially bypass UDFs by accessing the underlying Delta files directly if they have file-level permissions. Unity Catalog provides a superior, governance-focused approach to security enforcement.
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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-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.