What is the most suitable library for building a multi-step LLM-based workflow?

A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.

Which action would be most effective in mitigating the problem of offensive text outputs?

A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.

The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.

Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

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 Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.

How can they set up their Vector Search index to support this use case?

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services.

Given the following user input:

“I have been experiencing severe headaches and dizziness for the past two days.”

Which response is most appropriate for the chatbot to generate?

A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.

Which of the following components will NOT be useful in building such a chatbot?

A Generative AI Engineer received the following business requirements for an external chatbot.

The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.

What is an ideal workflow for such a chatbot?

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.

Which strategy for picking an embedding model should they choose?

A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.

Which combination of chaining components and configuration meets these requirements?

A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output “In Stock” if the product is available or only the term “Out of Stock” if not.

Which prompt will work to allow the engineer to respond to call classification labels correctly?

A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios

Which authentication method should they choose?

A Generative Al Engineer is building a production-ready LLM system which replies directly to customers. The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.

Which approach will do this?

A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A)

B)

C)

D)

A Generative Al Engineer is responsible for developing a chatbot to enable their company’s internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planningwhich data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:

call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives’ call resolution from fields call_duration and call start_time.

transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.

call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.

call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.

maintenance_schedule – a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.

They need sources that could add context to best identify ticket root cause and resolution.

Which TWO sources do that? (Choose two.)

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 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?