Databricks-DA-Assoc Securing Data Practice Question
An administrator needs to secure a table containing sensitive customer data. Which TWO options represent valid ways to restrict access using Unity Catalog?
⚠ Common exam trap
Candidates frequently select broad workspace-level admin roles or native cloud storage policies instead of focusing on Unity Catalog native primitives like GRANT and column masking.
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
✓
Use GRANT SELECT on the table to specific users or groups.
Unity Catalog provides robust security primitives including standard SQL GRANT/REVOKE commands and dynamic masking. By using these features, administrators can define who sees what data without altering the underlying data files. These two methods are the standard, best-practice approaches for ensuring that sensitive data is protected while maintaining a clean, performant analytics environment for end users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use GRANT SELECT on the table to specific users or groups.
Why this is correct
Standard SQL access controls allow administrators to explicitly define who can read a table. This is the fundamental mechanism for data governance in Unity Catalog, ensuring that only authorized users or service principals can execute queries against the sensitive data, adhering to the principle of least privilege.
- ✗
Delete the sensitive rows from the table manually.
Why it's wrong here
Manually deleting rows is a destructive action that results in permanent data loss. Data security should be achieved through access control policies, not by destroying the data itself. This approach prevents legitimate business use cases that require the full dataset for reporting, auditing, or machine learning model training.
- ✓
Implement column masking policies on sensitive columns.
Why this is correct
Masking policies provide a way to redact or obfuscate sensitive column values at query time. This allows users to access the table structure while preventing them from seeing the actual sensitive contents, providing a flexible and secure way to manage data access without creating multiple physical versions of the table.
- ✗
Rotate the workspace passwords weekly.
Why it's wrong here
Password rotation is a credential management policy, not a data security policy. While good for security hygiene, it does not restrict access to specific tables within Databricks. Access to data is governed by Unity Catalog permissions, regardless of the password policy applied to the user's account authentication.
- ✗
Disable the table for all users except the owner.
Why it's wrong here
Disabling a table is not a supported administrative action in Databricks. You manage access via permissions. Even if possible, it would be impractical for real-world scenarios where multiple teams need access to different levels of data. Security should be managed through granular permissions rather than binary enable/disable switches.
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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-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.