Databricks-DE-Assoc Databricks Intelligence Platform Practice Question
Which Databricks feature should be used to securely share data with external organizations without duplicating the data?
⚠ Common exam trap
Candidates often suggest copying data or using external tables/views. They overlook that Delta Sharing is specifically designed for secure, read-only external access without needing to move or replicate the data.
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
✓
Delta Sharing
Delta Sharing is a secure, open-protocol solution that allows organizations to share data with third parties without moving or copying the underlying files. By using Delta Sharing, the provider maintains a single source of truth while the consumer gains direct access to the data. This minimizes storage costs, ensures data consistency, and simplifies compliance management for multi-party data sharing scenarios.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog
Why it's wrong here
While Unity Catalog manages data governance and access control internally, it is not a direct tool for sharing data across separate organizations or cloud environments. Delta Sharing is the specific protocol designed for cross-organizational data exchange while maintaining the security policies defined by the Unity Catalog service.
- ✓
Delta Sharing
Why this is correct
Delta Sharing is the industry-standard, open protocol that enables secure, scalable data sharing without data duplication. It allows the recipient to access data directly from the provider's cloud storage, ensuring that the shared data remains up-to-date and consistent, which is ideal for external collaboration and B2B analytics.
- ✗
Databricks Workflows
Why it's wrong here
Workflows are used for orchestrating internal data processing tasks and dependencies. They are not intended for data sharing or collaboration with external parties. Using Workflows to share data would likely involve insecure methods like moving files to a shared location, which is inefficient and violates modern data governance practices.
- ✗
MLflow Model Registry
Why it's wrong here
The MLflow Model Registry is for managing the versioning and deployment of machine learning models. It does not provide mechanisms for sharing raw data or tables with external organizations. Its scope is entirely focused on the lifecycle management of AI assets, not on secure enterprise data exchange or distribution.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-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-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.