Oracle 1Z0-1127-24 Exam Dumps

The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.

Reference

Oracle ADS documentation

Guides on deploying AI models using Oracle Data Science services

Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.

Reference

Research papers on Parameter-Efficient Fine-Tuning (PEFT)

Technical documentation on fine-tuning techniques for large language models

Prompt injection (jailbreaking) involves manipulating the language model to bypass its built-in restrictions and protocols. The provided scenario (A) exemplifies this by asking the model to find a creative way to provide information despite standard protocols preventing it from doing so. This type of prompt is designed to circumvent the model's constraints, leading to potentially unauthorized or unintended outputs.

Reference

Articles on AI safety and security

Studies on prompt injection attacks and defenses

LangChain is an open-source framework that helps developers integrate Large Language Models (LLMs) into applications. It simplifies working with AI by handling data retrieval, memory, agents, and pipelines.

Key Features of LangChain:

Works with multiple LLMs, including OpenAI, Hugging Face, and enterprise solutions.

Simplifies AI-powered applications, such as chatbots, document summarization, and RAG-based search.

Provides tools for vector storage, indexing, and retrieval.

Enhances AI workflows by combining LLMs with external data sources.

Why Other Options Are Incorrect:

(A) JavaScript library -- LangChain is written in Python, not JavaScript.

(B) Ruby library -- LangChain is not a Ruby framework.

(D) Java library -- LangChain is not Java-based.

Oracle Generative AI Reference:

Oracle integrates LangChain for LLM-based applications in document search, AI chatbots, and workflow automation.

Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.

How RAG Works:

The model retrieves relevant documents from an external database.

Uses this retrieved information to generate factually grounded responses.

Reduces hallucinations, improving accuracy and context relevance.

Why Other Options Are Incorrect:

(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.

(B) is incorrect because RAG retrieves and uses data, not just stores it.

(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.

Oracle Generative AI Reference:

Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.