Configure AI/BI Genie Instructions for Data Discovery
Understanding AI/BI Genie in Unity Catalog
Within Azure Databricks, AI/BI Genie helps users discover and understand data using natural language queries. Genie uses metadata, descriptions, business context, and governance instructions stored within Unity Catalog to generate accurate and context-aware responses. The DP-750 exam expects candidates to understand how Genie instructions improve data discovery and help organizations align analytics with business terminology and governance requirements.
AI/BI Genie relies heavily on metadata quality. If catalogs, schemas, tables, and columns lack meaningful descriptions, Genie may return ambiguous or inaccurate responses. Engineers therefore configure instructions that explain business definitions, preferred query logic, sensitive data rules, filtering expectations, and reporting conventions.
For example, a sales organization may want Genie to always treat “current sales” as sales from the active financial year rather than historical data. Instructions help guide the AI model toward appropriate interpretations of business terms and reporting expectations.
Instructions can be configured at different levels within Unity Catalog. Catalog-level instructions apply broadly across the data domain. Schema-level instructions apply to a specific business area such as Finance or Marketing. Table-level instructions provide highly specific guidance for individual datasets.
This layered approach allows organizations to balance enterprise-wide consistency with detailed business-specific behavior.
Configuring Genie Instructions for Business Context
Engineers configure Genie instructions to improve search relevance and analytical consistency. Instructions often define key business terminology, approved calculations, data sensitivity guidance, and filtering recommendations.
For example, an organization may configure instructions stating that customer revenue calculations should exclude cancelled orders. Another instruction may define that regional reporting must use active sales territories only.
The following example demonstrates the concept of configuring catalog-level guidance.
ALTER CATALOG sales_catalog
SET AI_GENIE.INSTRUCTIONS =
'Use active sales territories only.
Always prioritize certified reporting tables.
Treat current year as the active financial year.';
These instructions help Genie understand organizational reporting standards. This capability improves the quality of natural language responses generated for analysts and business users.
Schema-level instructions may provide more detailed business guidance.
ALTER SCHEMA finance.gold
SET AI_GENIE.INSTRUCTIONS =
'Monthly revenue excludes refunded transactions.
Use transaction_date for revenue calculations.';
The DP-750 exam may test understanding of instruction hierarchy. More specific instructions typically override broader instructions when conflicts occur. Table-level guidance therefore takes precedence over schema-level or catalog-level guidance.
Governance and Best Practices for Genie Instructions
Governance remains an important consideration when configuring AI/BI Genie instructions. Organizations should ensure that Genie does not expose sensitive or restricted information accidentally. Instructions can guide Genie away from confidential columns or recommend approved datasets for reporting.
For example, a business may instruct Genie to avoid exposing salary data unless explicitly authorized. Another instruction may direct Genie to use certified Gold-layer reporting tables instead of raw Bronze ingestion datasets.
Clear and concise language improves instruction quality. Ambiguous instructions can produce inconsistent results. Engineers should use business-friendly terminology while avoiding unnecessary technical complexity.
Instructions should also be maintained actively. As business rules evolve, Genie instructions must remain aligned with updated reporting logic, governance policies, and organizational definitions. Outdated instructions can lead to inaccurate recommendations and inconsistent analytical outcomes.
Organizations commonly align Genie instructions with medallion architecture strategies. Bronze layers typically contain raw operational data, while Silver layers contain cleansed data. Gold layers expose business-ready curated datasets. Genie instructions often encourage users toward trusted Gold reporting tables for analytics workloads.
Lineage within Unity Catalog further improves governance by helping administrators understand how reports, dashboards, and notebooks depend on certified datasets and Genie-driven discovery workflows.
Using Genie for Effective Data Discovery
AI/BI Genie simplifies data exploration for business users who may not understand underlying table structures or SQL syntax. Users can ask natural language questions such as “Show monthly sales by region” or “Which tables contain customer information?” Genie interprets these requests using configured instructions and Unity Catalog metadata.
This capability reduces dependency on technical teams for basic analytics discovery. It also improves consistency because users receive recommendations aligned with organizational business definitions and governance standards.
The DP-750 exam expects candidates to understand how Genie instructions improve contextual awareness, support governance, and enhance natural language data discovery across Unity Catalog environments.
Links
Microsoft Certified: Azure Databricks Data Engineer Associate – Certifications | Microsoft Learn
What is a Genie Space – Azure Databricks | Microsoft Learn
Curate an effective Genie Space | Databricks on AWS
Example DP-750 Style Questions
Question 1
A data analyst asks Genie for sales information by region, but Genie includes inactive historical regions in the response. What type of instruction should the engineer configure?
Question 2
A company wants Genie to prioritize certified reporting datasets instead of raw ingestion tables. At which Unity Catalog layer would this guidance commonly be configured?
Question 3
An organization wants Genie to avoid exposing confidential salary information unless specifically authorized. What should the engineer include within Genie instructions?
Question 4
A user asks Genie to calculate monthly revenue, but the calculation incorrectly includes refunded transactions. What type of instruction should the engineer implement?
Question 5
A schema contains multiple reporting tables with similar names. Genie frequently recommends the wrong table to analysts. What improvement would most likely resolve this issue?
Question 6
An engineer configures conflicting instructions at catalog and table level. Which instructions will Genie most likely prioritize?
Question 7
A company updates its financial reporting definitions, but Genie continues returning outdated recommendations. What administrative action should the engineer perform?
Question 8
A business user asks Genie, “Which tables contain customer contact details?” Which Unity Catalog capability helps Genie answer this question accurately?
Answers
Answer 1
The engineer should configure instructions specifying active regional filtering guidance.
Answer 2
This guidance is commonly configured at the catalog or schema level.
Answer 3
The engineer should include governance guidance restricting sensitive data exposure.
Answer 4
The engineer should configure instructions defining approved revenue calculation logic.
Answer 5
The engineer should improve table descriptions and business metadata.
Answer 6
Genie will generally prioritize the more specific table-level instructions.
Answer 7
The engineer should review and update Genie instructions to reflect the new business definitions.
Answer 8
Unity Catalog metadata and descriptive business context help Genie identify appropriate tables accurately.