Databricks-ML-Pro Model Development Practice Question
When designing a model training pipeline, which TWO features of Unity Catalog best support compliance and model governance?
⚠ Common exam trap
Candidates often confuse workspace-level permissions or generic cloud storage policies with Unity Catalog's specific fine-grained governance capabilities like column-level access control and system-wide data lineage tracking.
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
✓
Column-level access control to restrict sensitive data visibility during feature engineering.
Unity Catalog acts as a centralized governance layer for all data and AI assets. By providing fine-grained access control and end-to-end lineage, it ensures that only authorized users can access sensitive training data and that the origin of every model can be traced back to the original source data, which is essential for meeting regulatory requirements and maintaining corporate security standards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Column-level access control to restrict sensitive data visibility during feature engineering.
Why this is correct
Column-level access control allows organizations to define granular permissions, ensuring that data scientists only see the columns necessary for their specific tasks. This is vital for compliance with data privacy regulations like GDPR or HIPAA, as it prevents the accidental exposure of sensitive PII during the feature development phase.
- ✗
Automated hyperparameter grid search for all registered datasets.
Why it's wrong here
Hyperparameter optimization is a modeling task handled by library-specific functions or Databricks AutoML, not by a data governance catalog. While Unity Catalog manages the data assets used for these tasks, it does not provide native functionality for running training algorithms or optimizing model performance parameters.
- ✓
System-wide data lineage tracking that maps from raw data to the final registered model.
Why this is correct
Lineage tracking is a core requirement for model governance. Being able to visualize the flow of data from raw tables through transformation pipelines to the final model allows teams to perform impact analysis and satisfy audit requirements by proving exactly which data sources influenced the model's predictive output.
- ✗
Built-in model serving endpoints for real-time inference.
Why it's wrong here
Model serving is handled by Model Serving features in Databricks, not by the Unity Catalog. While the Catalog stores the model metadata, the actual serving infrastructure and API endpoints are independent services configured separately to handle real-time inference requests and auto-scaling based on traffic demand.
- ✗
Automatic translation of SQL queries into Python code.
Why it's wrong here
Unity Catalog does not perform code translation. Its primary role is to provide a unified namespace, access control, and metadata management across the Databricks workspace. Providing code conversion would be outside the scope of a data and model governance framework, which is focused on security and transparency.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-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-ML-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-ML-Pro exam.