最新なDatabricks Databricks-Generative-AI-Engineer-Associate問題集（63題）、真実試験の問題を全部にカバー！

質問 1：
A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?
A. Input: Customer reviews; Output: Classify review sentiment
B. Input: Online chat logs; Output: Cancellation options
C. Input: Online chat logs; Output: Buttons that represent choices for booking details
D. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions
正解：C
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
A. vsc.get_index()
B. vsc.create_direct_access_index()
C. vsc.similarity_search()
D. vsc.create_delta_sync_index()
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?
A. Use a system prompt to instruct the model to be succinct in its answer
B. Use few snot prompting to instruct the model on expected output format
C. Use zero shot chain-of-thought prompting to prevent a verbose output format
D. Use zero shot prompting to instruct the model on expected output format
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?
A. 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.
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 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.
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.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
A.

B.

C.

D.

正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
A. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
B. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
C. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.
D. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
正解：C
解説: (Pass4Test メンバーにのみ表示されます)