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

質問 1

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 any information it sees will be about SnoPenAI, even if no data filtering is performed.

（B）Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.

（C）Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

（D）Consolidate all SnoPen AI related documents into a single chunk in the vector database.

正解：C 解答を投票する

解説: (JPNTest 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）Reduce the time that the users can interact with the LLM

（B）Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

（C）Ask the LLM to remind the user that the input is malicious but continue the conversation with the user

（D）Increase the amount of compute that powers the LLM to process input faster

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

（A）Final perplexity scores for the training of the model

（B）HuggingFace Leaderboard values for the base LLM

（C）Energy usage per query

（D）Number of customer inquiries processed per unit of time

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

（A）External tools

（B）Conversation Buffer Memory

（C）(Q)

（D）Vector Stores

（E）React Components

（F）Chat loaders

正解：B、D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

（A）BLEU metric

（B）ROUGE metric

（C）NDCG metric

（D）RECALL metric

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

（A）Reduce the maximum output tokens of the new model

（B）Retrain the response generating model using ALiBi

（C）Reduce the number of records retrieved from the vector database

（D）Decrease the chunk size of embedded documents

（E）Use a smaller embedding model to generate

正解：C、D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

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

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