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Databricks Certified Generative AI Engineer Associate

Last Update 4 days ago
Total Questions : 45

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Question # 1

A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they’re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.

Which model meets all the Generative Al Engineer’s needs in this situation?

Options:

A.  

Dolly 1.5B

B.  

OpenAI GPT-4

C.  

BGE-large

D.  

Llama2-70B

Discussion 0
Question # 2

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

Question # 2

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

A)

Question # 2

B)

Question # 2

C)

Question # 2

D)

Question # 2

Options:

A.  

Option A

B.  

Option B

C.  

Option C

D.  

Option D

Discussion 0
Question # 3

A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.

Which input/output pair will support their goal?

Options:

A.  

Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions

B.  

Input: Online chat logs; Output: Buttons that represent choices for booking details

C.  

Input: Customer reviews; Output: Classify review sentiment

D.  

Input: Online chat logs; Output: Cancellation options

Discussion 0
Question # 4

A company has a typical RAG-enabled, customer-facing chatbot on its website.

Question # 4

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

Options:

A.  

1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

B.  

1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM

C.  

1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model

D.  

1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

Discussion 0
Question # 5

A Generative Al Engineer is building a system which will answer questions on latest stock news articles.

Which will NOT help with ensuring the outputs are relevant to financial news?

Options:

A.  

Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

B.  

Increase the compute to improve processing speed of questions to allow greater relevancy analysis

C Implement a profanity filter to screen out offensive language

C.  

Incorporate manual reviews to correct any problematic outputs prior to sending to the users

Discussion 0
Question # 6

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

Options:

A.  

Pandas

B.  

TensorFlow

C.  

PySpark

D.  

LangChain

Discussion 0
Question # 7

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?

Options:

A.  

Respond with “In Stock” if the customer asks for a product.

B.  

You will be given a customer call transcript where the customer asks about product availability. The outputs are either “In Stock” or “Out of Stock”. Format the output in JSON, for example: {“call_id”: “123”, “label”: “In Stock”}.

C.  

Respond with “Out of Stock” if the customer asks for a product.

D.  

You will be given a customer call transcript where the customer inquires about product availability. Respond with “In Stock” if the product is available or “Out of Stock” if not.

Discussion 0
Question # 8

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?

Options:

A.  

Safety Guardrail

B.  

Security Guardrail

C.  

Contextual Guardrail

D.  

Compliance Guardrail

Discussion 0
Question # 9

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?

Options:

A.  

Limit the number of relevant documents available for the RAG application to retrieve from

B.  

Pick a smaller LLM that is domain-specific

C.  

Limit the number of queries a customer can send per day

D.  

Use the largest LLM possible because that gives the best performance for any general queries

Discussion 0
Question # 10

A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.

Which Python package should be used to extract the text from the source documents?

Options:

A.  

flask

B.  

beautifulsoup

C.  

unstructured

D.  

numpy

Discussion 0
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