Databricks-Generative-AI-Engineer-Associate 무료 덤프문제 온라인 액세스

A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

• A.Foundation Model APIs
• B.DatabrickslQ
• C.AutoML
• D.Feature Serving

A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?

• A.Compare the Levenshtein distances of returned results against a representative sample of test inputs
• B.Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
• C.Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
• D.Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

A generative AI engineer is deploying an AI agent authored with MLflow's ChatAgent interface for a retail company's customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

• A.No metrics are automatically captured
• B.Quality metrics like correctness and guideline adherence
• C.Both operational and quality metrics
• D.Operational metrics like request volume, latency, and errors

A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?

• A.User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
• B.Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving
• C.Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
• D.Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving

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