1Z0-1127-25試験無料問題集（90題）「Oracle Cloud Infrastructure 2025 Generative AI Professional 認定」

出題：1

Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It requires a large temperature setting to ensure diverse word selection.

B. It selects words based on a flattened distribution over the vocabulary.

C. It chooses words randomly from the set of less probable candidates.

D. It picks the most likely word at each step of decoding.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. A keyword search-based AI that responds based on specific keywords identified in customer queries.

B. An LLM dedicated to generating text responses without external data integration.

C. A pre-trained LLM model from Cohere or OpenAI.

D. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When you want to optimize the model without any instructions

B. When the LLM already understands the topics necessary for text generation

C. When the LLM does not perform well on a task and the data for prompt engineering is too large

D. When the LLM requires access to the latest data for generating outputs

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

A. StreamlitChatMessageHistory can be used in any type of LLM application.

B. A given StreamlitChatMessageHistory will NOT be persisted.

C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.

D. A given StreamlitChatMessageHistory will not be shared across user sessions.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

What is prompt engineering in the context of Large Language Models (LLMs)?

A. Adding more layers to the neural network

B. Adjusting the hyperparameters of the model

C. Iteratively refining the ask to elicit a desired response

D. Training the model on a large dataset

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?

A. 20 unit hours

B. 25 unit hours

C. 40 unit hours

D. 30 unit hours

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" limits token selection based on the sum of their probabilities.

B. "Top p" determines the maximum number of tokens per response.

C. "Top p" assigns penalties to frequently occurring tokens.

D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

A. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.

B. They increase the cost due to the need for real-time updates.

C. They are more expensive but provide higher quality data.

D. They require frequent manual updates, which increase operational costs.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Translation models

B. Embedding models

C. Summarization models

D. Generation models

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

1Z0-1127-25試験無料問題集「Oracle Cloud Infrastructure 2025 Generative AI Professional 認定」