A Generative AI Engineer is building a multi-turn chat app with LangGraph on Databricks. The app must persist chat history-messages, roles, timestamps, and session IDs-for many concurrent users, support SQL queries, and stay governed in Unity Catalog. The engineer also wants ACID guarantees, low-latency reads and writes, and an easy way to sync chat data into Delta tables for analytics and model training.
Which approach fits these requirements?

• A. Store conversation history in MLflow runs and retrieve it via the MLflow Tracking API inside LangGraph nodes.
• B. Use Lakebase with a chat_history table wired to a Postgres-backed LangGraph checkpoint/memory component and enable synchronization from Lakebase into Delta tables.
• C. Use a custom in-memory LangGraph state store running on the Databricks cluster driver, and periodically snapshot the state to JSON files in DBFS.
• D. Write each turn from a custom LangGraph node directly into a Delta table with Spark append, then query history via Spark SQL on every request.

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A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

• A. Implement AI guardrails to allow users to configure and enforce compliance
• B. Leverage Databricks access control lists (ACLs) to configure permissions for accessing models
• C. Use secure model features with Databricks Feature Store
• D. Apply attribute-based access controls (ABAC) to limit unauthorized access

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A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?

• A. Repetition of responses
• B. Diversity of responses
• C. Randomness
• D. Lack of relevance

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A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

• A. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
• B. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
• C. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
• D. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.

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A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

• A. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
• B. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.
• C. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.
• D. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

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