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

質問 1：
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?
A. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section
B. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
C. First create a unique identifier for each document, then save to a Delta table
D. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
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.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?
A. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
B. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
C. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
D. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
A. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
C. Consolidate all SnoPen AI related documents into a single chunk in the vector database.
D. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?
A. DBRX
B. MPT-30B
C. DistilBERT
D. Llama2-70B
正解：D
解説: (Topexam メンバーにのみ表示されます)