Databricks-Generative-AI-Engineer-Associate 無料問題集「Databricks Certified Generative AI Engineer Associate」

（A）Deploy the model using pay-per-token throughput as it comes with cost guarantees

（B）Switch to using External Models instead

（C）Change to a model with a fewer number of parameters in order to reduce hardware constraint issues

（D）Throttle the incoming batch of requests manually to avoid rate limiting issues

（A）Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.

（B）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.

（C）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.

（D）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.

（A）Use a different semantic similarity search algorithm

（B）Implement caching for frequently asked questions

（C）Use a different LLM to improve the generated response

（D）Assess the quality of the retrieved context

（A）Split the LLM output by newline characters to truncate away the summarization explanation.

（B）Provide few shot examples of desired output format to the system and/or user prompt.

（C）Tune the chunk size of news articles or experiment with different embedding models.

（D）Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.

（A）Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

（B）Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

（C）Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.

（D）Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.