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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q75-Q80):

NEW QUESTION # 75
What does in-context learning in Large Language Models involve?

A. Adding more layers to the model
B. Conditioning the model with task-specific instructions or demonstrations
C. Training the model using reinforcement learning
D. Pretraining the model on a specific domain

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model's pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context.
OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt-based customization.

NEW QUESTION # 76
What is LangChain?

A. A Ruby library for text generation
B. A Python library for building applications with Large Language Models
C. A JavaScript library for natural language processing
D. A Java library for text summarization

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.

NEW QUESTION # 77
How does a presence penalty function in language model generation when using OCI Generative AI service?

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

Answer: A

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 # 78
Which is NOT a built-in memory type in LangChain?

A. ConversationBufferMemory
B. ConversationSummaryMemory
C. ConversationImageMemory
D. ConversationTokenBufferMemory

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

NEW QUESTION # 79
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
B. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
C. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
D. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false-PEFT doesn't replace architecture. Option C is incorrect, as PEFT isn't trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.
OCI 2025 Generative AI documentation likely compares Fine-tuning and PEFT under customization techniques.
Here is the next batch of 10 questions (31-40) 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 # 80
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High efficient 1Z0-1127-25 Guide Torrent Practice Materials: Oracle Cloud Infrastructure 2025 Generative AI Professional - Exam4Docs

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q75-Q80):

Quiz 2025 1Z0-1127-25: Latest Valid Dumps Oracle Cloud Infrastructure 2025 Generative AI Professional Pdf

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