Databricks-Generative-AI-Engineer-Associateの無料問題集（47題）、Databricks-Generative-AI-Engineer-Associate認定試験参考書

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

試験コード：Databricks-Generative-AI-Engineer-Associate

試験名称：Databricks Certified Generative AI Engineer Associate

最近更新時間：2024-10-15

問題と解答：全47問

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無料問題集Databricks-Generative-AI-Engineer-Associate 資格取得

質問 1：
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
A. Dolly 1.5B
B. OpenAI GPT-4
C. BGE-large
D. Llama2-70B
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
A. Increase the amount of compute that powers the LLM to process input faster
B. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user
C. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
D. Reduce the time that the users can interact with the LLM
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
A. Use the largest LLM possible because that gives the best performance for any general queries
B. Limit the number of relevant documents available for the RAG application to retrieve from
C. Limit the number of queries a customer can send per day
D. Pick a smaller LLM that is domain-specific
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?
A. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
B. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
C. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
D. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
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. Thematch 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 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.
B. 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.
C. 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.
D. 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.
正解：A
解説: (Topexam メンバーにのみ表示されます)

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Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題:

1. A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn't hallucinate or leak confidential data.
Which approach should NOT be used to mitigate hallucination or confidential data leakage?

A) Limit the data available based on the user's access level
B) Add guardrails to filter outputs from the LLM before it is shown to the user
C) Fine-tune the model on your data, hoping it will learn what is appropriate and not
D) Use a strong system prompt to ensure the model aligns with your needs.

2. A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?

A) The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
B) For the question-answering application, prompt engineering and an LLM are required to generate answers.
C) For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
D) For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.

3. A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A) You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "[email protected]", "order_id":
"RE987D"}
B) You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
C) You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
D) You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.

4. A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

A) flask
B) unstructured
C) beautifulsoup
D) numpy

5. 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 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
D) 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

質問と回答：

質問 # 1
正解： C

質問 # 2
正解： C

質問 # 3
正解： A

質問 # 4
正解： B

質問 # 5
正解： D