Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Test 2 — 15 Questions

Question 1

Which TWO of the following are best practices for writing instructions in an AI/BI Genie space?

Accepted Answer

Provide clear, concise business logic definitions. Effective Genie instructions are concise, specific, and structured. They should prioritize clarity to prevent ambiguity and ensure the model consistently follows business rules. By focusing on explicit examples and avoiding irrelevant information, developers help the LLM maintain high accuracy and reduce the risk of hallucination, which is vital for building trust with business users who rely on the provided insights.

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Include historical SQL queries for every possible outcome

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Include sensitive security credentials for authentication

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Use long-form essay style descriptions for all tables

Question 2

An analyst wants to ensure that a Genie space only uses the most recent data for all queries. How can this be achieved?

Accepted Answer

Use instructions to mandate filters on date columns. To enforce data freshness, the analyst should instruct the Genie space to use views or filter conditions that point to the latest partitions or versioned tables. By incorporating this logic into the instructions, the developer ensures that the model always applies the necessary filter clauses (like 'WHERE date = CURRENT_DATE') to every generated query, maintaining data integrity without requiring manual input from the users.

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Set the SQL Warehouse to auto-terminate every hour

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Delete old data from the Unity Catalog tables

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Rename tables whenever new data arrives

Question 3

What happens if a user submits a question that requires data from a table NOT explicitly defined in the Genie space configuration?

Accepted Answer

The model will deny the request due to lack of access. Genie spaces are designed with a security-first approach. If a table is not listed in the space's configuration, the model is physically prevented from accessing it. This ensures that users cannot 'force' the AI to query unauthorized or sensitive data outside the intended analytical scope, protecting the organization from data leakage or unauthorized access to proprietary information stored in other parts of the Unity Catalog.

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The model will try to guess the schema of the table

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The model will use the table anyway if the user has access

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The query will execute but return null results

Question 4

Why should an analyst use the 'feedback' feature provided in an AI/BI Genie space interface?

Accepted Answer

To improve the accuracy of future answers. The feedback mechanism is crucial for the iterative improvement of the Genie space. When users rate answers as 'helpful' or 'not helpful,' they provide signal that Databricks uses to refine the model's performance over time. This continuous learning process ensures that the assistant becomes more accurate, more relevant, and better aligned with the specific business context, ultimately providing higher value to the entire user organization.

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To log a support ticket with Databricks Engineering

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To increase the SQL Warehouse query limit

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To export the answer as a CSV file

Question 5

A Data Analyst is configuring an AI/BI Genie Space to allow business users to query sales data. The analyst needs to ensure the model understands specific business logic, such as how to calculate 'Net Revenue' from raw columns. Where should the analyst define this logic to ensure Genie consistently applies these business rules across all user queries?

Accepted Answer

Define the logic in the SQL view metadata or table comments within Unity Catalog.. Defining business logic in the underlying Databricks SQL Warehouse or Unity Catalog perspective is the most reliable way to guide Genie. By providing clear descriptions and semantic definitions within the catalog or view metadata, the LLM-driven engine can correctly interpret business terminology. This ensures that non-technical users receive consistent, accurate results without needing to write complex SQL, directly impacting the trust and adoption of the AI/BI solution within the organization.

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Add the logic as a comment in the user's prompt history.

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Hardcode the business logic into the Genie Space configuration UI.

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Require users to provide the formula in every natural language query.

Question 6

Which primary resource must be granted to users for them to successfully interact with an AI/BI Genie Space to generate insights?

Accepted Answer

CAN_USE permission on the Genie Space and SELECT access on underlying data.. Users require explicit access permissions to both the Genie Space itself and the underlying data assets (tables or views) it exposes. Without sufficient SELECT privileges on the underlying Unity Catalog assets, the Genie engine cannot execute the SQL generated to answer natural language questions. Managing these permissions centrally through Unity Catalog ensures that data governance and security policies are consistently applied, even when users are interacting via natural language interfaces.

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CAN_EDIT permission on the Genie Space.

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Global admin access within the Databricks Workspace.

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Access to the underlying SQL Warehouse only.

Question 7

Refer to the exhibit. A developer is configuring a Genie Space. The model is still including discounts in its revenue calculations despite the instruction. What is the most likely cause?

Accepted Answer

The model lacks sufficient metadata or schema constraints on the 'discount' column.. The AI model is likely relying on the underlying schema metadata or the actual data distribution more heavily than the provided system instructions. When instructions are contradictory to standard SQL column names or common business logic, the model requires more explicit guidance. Improving the precision of the instructions, such as specifying columns to exclude, or modifying the view definition, is necessary to override the model's default behavior.

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The temperature is set too low.

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The Genie Space requires a higher temperature setting to understand complex exclusions.

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The 'data_sources' array is missing the 'discounts' table.

Question 8

Which TWO actions should an analyst take to improve the accuracy of an AI/BI Genie Space when users report that it frequently hallucinates metric definitions?

Accepted Answer

Provide clear, concise system instructions that define key metrics.. Improving AI/BI Genie accuracy involves a dual approach: tightening the instruction set and refining the data layer. By providing specific, clear instructions, the analyst reduces ambiguity. Simultaneously, creating curated views in Unity Catalog enforces a 'source of truth,' limiting the model's ability to interpret ambiguous raw columns. These steps collectively ground the LLM, ensuring that it generates reliable SQL based on verified organizational definitions rather than probabilistic guesses.

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Increase the number of tables available to the Genie Space.

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Enable 'auto-discovery' of all columns in the Unity Catalog tables.

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Use a higher temperature setting to allow for more flexible interpretations.

Question 9

An analyst notices that the AI/BI Genie Space is generating queries that take too long to execute. The underlying data is stored in partitioned tables. What is the most effective way to help the Genie generate more performant SQL?

Accepted Answer

Modify the system instructions to require filtering on partition keys.. Genie models are highly responsive to context. By adding explicit instructions about filtering on partition columns, such as 'always filter by date or region,' the model learns to include these constraints in its generated SQL. This ensures that the generated queries prune data partitions effectively, significantly reducing query runtime and resource consumption on the SQL Warehouse, which directly improves the end-user experience for all business users.

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Instruct the model to use 'SELECT *' to get all data at once.

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Increase the SQL Warehouse cluster size to handle larger queries.

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Use a different LLM model within the Genie settings.

Question 10

You are auditing access to several AI/BI Genie Spaces. You discover that a group of users can ask questions but cannot see the underlying source tables in the Catalog Explorer. Is this a functional issue?

Accepted Answer

No, this is a secure configuration that follows the principle of least privilege.. This is expected behavior and a sound security practice. Genie acts as an intermediary; users do not need to be able to browse or query the source tables directly to use the Genie interface. By decoupling the interface from direct data access, you simplify the user experience and maintain stricter control over how data is accessed, preventing users from bypassing the Genie's curated, secure environment.

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Yes, users must have direct access to source tables to use Genie.

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No, but it will prevent the Genie from generating any results.

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Yes, the users will be unable to run any queries through Genie.

Question 11

Which THREE of the following are essential components of a well-architected AI/BI Genie Space setup?

Accepted Answer

Detailed documentation in the Unity Catalog table/column comments.. A robust Genie Space setup relies on clear semantic metadata, optimized data access, and controlled user permissions. By defining clear table descriptions in Unity Catalog, you provide the LLM with the context needed for accurate SQL generation. Combining this with granular permissions and well-crafted system instructions ensures the solution is both secure and reliable. These three pillars create a scalable environment that allows users to self-serve insights while maintaining strict organizational data governance.

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Providing the end-users with direct read access to all raw source tables.

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Granting all users 'owner' privileges on the Genie Space.

Question 12

An analyst is setting up a Genie Space to handle financial reporting. They want the model to always group results by 'fiscal_quarter'. How should they enforce this requirement?

Accepted Answer

Add a constraint to the system instructions.. Forcing specific groupings is best achieved through system instructions in the Genie Space configuration. By explicitly stating 'Always group by fiscal_quarter for all time-based queries,' the analyst provides a mandatory rule for the model. This guarantees that every time-series query generated by the AI aligns with the company's fiscal calendar, preventing the model from defaulting to calendar months and ensuring reporting consistency across the organization.

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Update the default table sort order in the catalog.

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Force users to type 'group by fiscal_quarter' in their prompts.

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Create a view that excludes the 'date' column.

Question 13

A business user asks a Genie Space, 'Show me the revenue by product.' The model returns an error. What is the most common reason for this failure in a properly configured environment?

Accepted Answer

The model cannot distinguish between multiple columns that could mean 'revenue'.. The most common failure in Genie occurs when the model cannot uniquely identify the requested columns or the business logic for 'revenue' is ambiguous. If multiple columns could represent revenue or if the product identifier is unclear, the model will struggle. Clarifying the schema via table comments and standardizing the definition of key metrics in the data layer is the standard fix for this ambiguity issue.

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The user does not have access to the underlying SQL Warehouse.

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The Genie Space is currently set to 'read-only' mode.

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The user is using the wrong language for the query.

Question 14

Refer to the exhibit. The Genie Space output is consistently truncated. What is the most likely cause?

Accepted Answer

The 'max_tokens' limit is too low for the complexity of the output.. The 'max_tokens' parameter is set to 500, which is relatively small for complex SQL generation tasks. If the generated query is long, or if the model provides a detailed explanation along with the SQL, it will hit this limit and truncate the response. Increasing the 'max_tokens' limit will allow the model to provide complete responses, ensuring that the SQL is valid and the context provided is not cut off mid-sentence.

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The temperature is too low, causing the model to finish early.

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The model version is incompatible with the SQL Warehouse.

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The SQL Warehouse is timing out during the query generation.

Question 15

When designing an AI/BI Genie Space, how can an analyst prevent the model from accessing PII (Personally Identifiable Information) columns in a sensitive table?

Accepted Answer

Create a filtered view in Unity Catalog that excludes sensitive columns.. The best approach to prevent PII exposure is to create a secure view in Unity Catalog that excludes the PII columns and point the Genie Space to that view instead of the raw table. This effectively enforces data masking and access control at the data layer. By only exposing the non-sensitive data required for analysis, you maintain privacy compliance while still allowing the AI to generate useful insights for the business.

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Add a disclaimer in the system instructions to 'not use PII'.

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Use a specialized, private LLM that has no knowledge of PII.

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Set the temperature to 0 to minimize hallucinations involving PII.