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Free CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE – Databricks CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Test Question

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Showing 7–9 of 10 questions

Question 7 (Topic 1)

A Generative AI Engineer received the following business requirements for an internal chatbot. The internal chatbot needs to know what types of questions the user asks and route them to appropriate models to answer the questions. For example, the user might ask about historical failure rates of a specific electrical part. Another user might ask about how to troubleshoot a piece of electrical equipment. Available data sources include a database of electrical equipment PDF manuals and also a table with information on when an electrical part experiences failure. Which workflow supports such a chatbot?

Select an option, then click Submit answer.

  • ○ Parse the electrical equipment PDF manuals into a table of question and response pairs. That way, the same chatbot can query tables easily to answer questions about both historical failure rates and equipment troubleshooting.
  • ○ The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s a historical failure rate question, send the query to a text-to-SQL model. If it’s a troubleshooting question, then send the query to another model that summarizes the equipment-specific document and generates the response.
  • ○ There should be two different chatbots handling different types of user queries.
  • ○ The table with electrical part failures should be converted into a text document first. That way, the same chatbot can use the same document retrieval process to generate answers regardless of question types.
Question 8 (Topic 1)

A Generative AI Engineer has been reviewing issues with their company's LLM based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?

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  • ○ It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.
  • ○ It allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.
  • ○ It allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.
  • ○ It reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.
Question 9 (Topic 1)

An AI developer team wants to fine tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs, and are using Huggingface model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection? (Choose two.)

Select an option, then click Submit answer.

  • ○ Big Code Models Leaderboard
  • ○ Number of model parameters
  • ○ MTEB Leaderboard
  • ○ Chatbot Arena Leaderboard
  • ○ Number of model downloads last month