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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q88-Q93):

NEW QUESTION # 88
What does the Ranker do in a text generation system?

A. It evaluates and prioritizes the information retrieved by the Retriever.
B. It sources information from databases to use in text generation.
C. It generates the final text based on the user's query.
D. It interacts with the user to understand the query better.

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 # 89
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

A. A given StreamlitChatMessageHistory will NOT be persisted.
B. A given StreamlitChatMessageHistory will not be shared across user sessions.
C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
D. StreamlitChatMessageHistory can be used in any type of LLM application.

Answer: D

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 # 90
What is LCEL in the context of LangChain Chains?

A. An older Python library for building Large Language Models
B. A declarative way to compose chains together using LangChain Expression Language
C. A programming language used to write documentation for LangChain
D. A legacy method for creating chains in LangChain

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.

NEW QUESTION # 91
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" assigns penalties to frequently occurring tokens.
B. "Top p" limits token selection based on the sum of their probabilities.
C. "Top p" selects tokens from the "Top k" tokens sorted by probability.
D. "Top p" determines the maximum number of tokens per response.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

NEW QUESTION # 92
How does a presence penalty function in language model generation when using OCI Generative AI service?

A. It penalizes all tokens equally, regardless of how often they have appeared.
B. It applies a penalty only if the token has appeared more than twice.
C. It penalizes a token each time it appears after the first occurrence.
D. It only penalizes tokens that have never appeared in the text before.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.

NEW QUESTION # 93
......

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q88-Q93):

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