Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Question
Which component of an AI/BI Genie space is responsible for defining the scope of data available to a user and ensuring that the natural language model only references authorized tables?
⚠ Common exam trap
Candidates often look for a specific 'security tab' or 'access menu' instead of recognizing that the Genie space configuration itself acts as the boundary for data access and LLM scope.
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
✓
The Genie space instructions and metadata definition
Genie spaces rely on the definition of data instructions and schema mappings within the space configuration. By explicitly linking specific tables and columns, the system enforces access control and ensures the LLM does not hallucinate using external data. This metadata-driven approach is critical for maintaining data governance and security while enabling self-service analytics for business users within the Databricks ecosystem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Unity Catalog Data Explorer integration
Why it's wrong here
Data Explorer provides visibility into assets but does not programmatically constrain the LLM's reasoning scope inside a Genie space. Genie spaces require explicit table instructions to determine which metadata is exposed for query generation, making Data Explorer a secondary tool rather than the core control mechanism.
- ✓
The Genie space instructions and metadata definition
Why this is correct
Instructions and table definitions act as the grounding layer for the Genie space. By specifying allowed tables and providing business context in the instructions, you restrict the LLM to a specific subset of Unity Catalog assets, effectively managing scope and preventing unauthorized query generation across the broader catalog.
- ✗
The Databricks SQL Warehouse access policy
Why it's wrong here
SQL Warehouse access policies dictate who can execute queries, not what data the AI model can reason over. Even if a user has access to a warehouse, the Genie space logic determines which tables the LLM is allowed to interact with during the natural language query process.
- ✗
The workspace-level workspace AI configuration
Why it's wrong here
Workspace settings control general AI features, but they do not offer the granular table-level scoping required for a Genie space. Genie spaces are isolated environments that require specific, per-space configuration to ensure that the natural language interaction is bounded by the intended business datasets.
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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.