Certified Generative AI Engineer Associate Practice Test

Certified Generative AI Engineer Associate Practice Test practice questions, page 4 of 5, with answer review and explanations.

Question 16

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?

  1. Respond with “In Stock” if the customer asks for a product.
  2. 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”}.
  3. Respond with “Out of Stock” if the customer asks for a product.
  4. 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.

Question 17

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?

  1. Dolly 1.5B
  2. OpenAI GPT-4
  3. BGE-large
  4. Llama2-70B

Question 18

A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here’s a sample email: They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy. Which prompt will do that?

  1. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
  2. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format. Here’s an example: {“date”: “April 16, 2024”, “sender_email”: “sarah.lee925@gmail.com”, “order_id”: “RE987D”}
  3. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
  4. You will receive customer emails and need to extract date, sender email, and order IReturn the extracted information in JSON format.

Question 19

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?

  1. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
  2. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
  3. For the question-answering application, prompt engineering and an LLM are required to generate answers.
  4. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.

Question 20

A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team’s latest standings. How could the Generative AI Engineer best design these capabilities into their system?

  1. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
  2. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
  3. Instruct the LLM to respond with “RAG”, “API”, or “TABLE” depending on the query, then use text parsing and conditional statements to resolve the query.
  4. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.