Databricks-DE-Assoc Governance and Security Practice Question
Which security feature in Databricks allows administrators to mask sensitive data, such as email addresses or social security numbers, in query results based on user-defined functions?
⚠ Common exam trap
Candidates confuse static table partitioning or column dropping with Dynamic Data Masking, which obscures sensitive field values on-the-fly based on user identity.
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
Dynamic Data Masking allows you to define a masking function that hides or transforms data based on the user's role. By applying these functions to specific columns in a view or table, sensitive information is obscured in the results returned to the user, providing an effective way to maintain compliance with data privacy regulations like GDPR or HIPAA.
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
Why it's wrong here
Row-Level Security filters the entire record returned to the user based on a boolean condition. It does not modify individual column values within a row, making it unsuitable for masking sensitive data fields while still allowing users to see the rest of the record's information.
- ✓
Dynamic Data Masking
Why this is correct
Dynamic Data Masking allows you to apply a masking function to columns, ensuring that users only see redacted or transformed versions of sensitive data. This is the precise feature designed to protect PII, such as emails or IDs, while still allowing data analysis on the non-sensitive parts.
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Table ACLs
Why it's wrong here
Table ACLs are used to control access at the object level, determining who can read or modify a table. They do not provide the capability to alter the contents of the data within the columns, which is necessary for masking sensitive information like personal identifiers.
- ✗
Workspace-level isolation
Why it's wrong here
Workspace-level isolation separates data and compute resources between different workspaces. It does not provide fine-grained control over the data within a table or the ability to mask individual columns. Masking must be implemented within the data layer using Unity Catalog features, not by separating workspaces.
About these practice questions
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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-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.