Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Question
A data analyst is setting up a new Genie space. Which TWO of the following are prerequisites for the successful creation and usage of a Genie space?
⚠ Common exam trap
Candidates often forget that serverless compute is a mandatory prerequisite, sometimes wrongly assuming that standard SQL warehouses or manual compute provisioning are sufficient for Genie space functionality.
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
✓
A Unity Catalog-enabled workspace
Genie spaces require a foundational environment where data is discoverable and executable. Unity Catalog provides the necessary governance and metadata, while a SQL Warehouse provides the compute resources to execute the generated code. Without these, the Genie space lacks both the data access layer and the processing capability required to answer user questions, making these two components absolute necessities for the feature to function correctly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A Unity Catalog-enabled workspace
Why this is correct
Unity Catalog is mandatory because it provides the unified metadata and governance required for the LLM to discover and reason over tables. Without Unity Catalog, the Genie space cannot resolve table schemas, identify relationships, or enforce the data access controls necessary for safe natural language interactions.
- ✓
A running Serverless SQL Warehouse
Why this is correct
A SQL Warehouse is the compute engine that processes the SQL generated by the Genie space. Serverless warehouses are preferred for their fast startup times and performance, ensuring that when an analyst asks a question, the execution happens quickly without manual intervention or long resource cold-start delays.
- ✗
A pre-trained custom Large Language Model
Why it's wrong here
Genie spaces use managed AI models provided by Databricks, so users do not need to train or provide their own LLMs. This abstraction simplifies the setup process, allowing analysts to focus on data context rather than the complexities of model deployment, fine-tuning, or local hosting requirements.
- ✗
A local Python environment with Genie SDK
Why it's wrong here
Genie spaces are managed within the Databricks UI and do not require local SDKs or Python environments. Interaction happens directly through the web interface, making the feature accessible to business users without needing software engineering expertise or local development setups on their own machines.
- ✗
A Delta Live Table pipeline with high availability
Why it's wrong here
While Delta Live Tables are excellent for data engineering, they are not a prerequisite for Genie. Genie spaces can query any table registered in Unity Catalog, regardless of how it was ingested, provided the user has the appropriate read permissions for the datasets in question.
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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.