Databricks Databricks-Generative-AI-Engineer-Associate 問題集 : Databricks Certified Generative AI Engineer Associate

A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?

• A. 1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use COT reasoning to determine the appropriate agent
• B. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
• C. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
• D. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools

A Generative AI Engineer is integrating Mosaic AI Vector Search into a Retrieval-Augmented Generation (RAG) system. The source data, comprising simple text entries, is stored in a Delta table. To simplify the workflow, the engineer plans to use an embedding model served via a Mosaic AI Model Serving endpoint to automatically compute embeddings during data synchronization from the Delta table to the vector search index.
Which method should the engineer use to achieve this integration?

• A. Delta Sync index with managed embeddings.
• B. Delta Sync index with self-managed embeddings.
• C. Hybrid embedding computing.
• D. Direct Vector Access index.

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?

• A. 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.
• B. 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.
• C. 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.
• D. It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.

Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?

• A. Use a different LLM to improve the generated response
• B. Implement caching for frequently asked questions
• C. Assess the quality of the retrieved context
• D. Use a different semantic similarity search algorithm

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user' s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

• A. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.
• B. Change embedding models and compare performance.
• C. Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.
• D. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.
  Choose the strategy that gives the best performance metric.
• E. Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.