Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
You are processing sensitive PII data in a Delta table. You need to ensure that specific columns containing PII are not readable by general data analysts while maintaining the ability to perform aggregate analysis on those rows. Which feature should you implement?
⚠ Common exam trap
Candidates often suggest column-level encryption or physical data removal. These are overkill and do not allow for the necessary aggregate analysis required by the prompt, whereas Dynamic Views provide the required flexibility.
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 Dynamic Views with functions like 'mask_hash' or 'case'.
Databricks Unity Catalog provides robust data governance, including column-level security through Dynamic Views. By defining a view that masks or filters data based on the user's role, you ensure PII protection while allowing data exploration. Managing access control via Unity Catalog is a standard professional requirement for maintaining compliance (GDPR/CCPA) and ensuring that sensitive information remains secure within the enterprise data architecture.
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 the 'ALTER TABLE DROP COLUMN' command.
Why it's wrong here
Dropping a column physically removes it from the table schema, which is permanent and destructive. This makes it impossible for authorized users to perform necessary analysis on the PII data, and it is not a suitable solution for protecting data while still allowing valid access for authorized personnel.
- ✓
Apply Dynamic Views with functions like 'mask_hash' or 'case'.
Why this is correct
Dynamic Views allow the definition of access policies based on the session user's role or identity. By using functions such as masking or conditional logic, you can obscure PII for unauthorized users while keeping the underlying data intact for administrative roles or authorized analytical processes as required.
- ✗
Implement row-level security using Spark configurations.
Why it's wrong here
Row-level security filters which rows are returned, but it does not address column-level security requirements. If the goal is to hide specific columns like 'PII', row-level filtering will still return the column, even if the row content is restricted, failing the requirement to protect the column itself.
- ✗
Encrypt the entire storage bucket using cloud-native tools.
Why it's wrong here
Encryption at rest protects data against physical disk theft or unauthorized access to the storage account, but it does not control query-time access within the Databricks environment. Once the user is authenticated in Databricks, the engine decrypts the data and presents it to the user without restriction.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-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-DE-Pro 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-Pro exam.