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[General] Achieving Exam Success with TestkingPass Oracle 1Z0-1127-25 Dumps

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【General】 Achieving Exam Success with TestkingPass Oracle 1Z0-1127-25 Dumps

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q62-Q67):NEW QUESTION # 62
What does the Ranker do in a text generation system?
  • A. It evaluates and prioritizes the information retrieved by the Retriever.
  • B. It generates the final text based on the user's query.
  • C. It interacts with the user to understand the query better.
  • D. It sources information from databases to use in text generation.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 63
How does a presence penalty function in language model generation?
  • A. It applies a penalty only if the token has appeared more than twice.
  • B. It penalizes only tokens that have never appeared in the text before.
  • C. It penalizes a token each time it appears after the first occurrence.
  • D. It penalizes all tokens equally, regardless of how often they have appeared.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

NEW QUESTION # 64
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
  • A. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • B. A diffusion model that specializes in producing complex outputs.
  • C. A language model that operates on a token-by-token output basis
  • D. A Large Language Model-based agent that focuses on generating textual responses
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.
OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.

NEW QUESTION # 65
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?
  • A. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
  • B. StreamlitChatMessageHistory can be used in any type of LLM application.
  • C. A given StreamlitChatMessageHistory will not be shared across user sessions.
  • D. A given StreamlitChatMessageHistory will NOT be persisted.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.
OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.

NEW QUESTION # 66
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
  • A. Limiting the model to only k possible outcomes or answers for a given task
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Explicitly providing k examples of the intended task in the prompt to guide the model's output
  • D. Providing the exact k words in the prompt to guide the model's response
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

NEW QUESTION # 67
......
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