Online Access Free Databricks-Generative-AI-Engineer-Associate Practice Test

A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?

• A.Compliance Guardrail
• B.Security Guardrail
• C.Contextual Guardrail
• D.Safety Guardrail

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?

• A.Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
• B.Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.
• C.Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
• D.Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

• A.Ask the LLM to remind the user that the input is malicious but continue the conversation with the user
• B.Increase the amount of compute that powers the LLM to process input faster
• C.Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
• D.Reduce the time that the users can interact with the LLM

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 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.
• B.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.
• C.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.
• 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.

A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?

• A.Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.
• B.Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.
• C.Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.
• D.Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.