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Exam 1z0-1127-24 Quizzes | 1z0-1127-24 Test Questions

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

Topic 2

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

Topic 3

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q19-Q24):

NEW QUESTION # 19
What is the purpose of Retrievers in LangChain?

A. To retrieve relevant information from knowledge bases
B. To break down complex tasks into smaller steps
C. To train Large Language Models
D. To combine multiple components into a single pipeline

Answer: A

Explanation:
Retrievers in LangChain serve the primary function of fetching relevant data from an external knowledge base or database to enhance the performance of Large Language Models (LLMs).
How Retrievers Work:
They retrieve documents, embeddings, or structured data that might be relevant to a given query.
Used in Retrieval-Augmented Generation (RAG) models to fetch real-time data.
Improves model responses by providing accurate and up-to-date knowledge.
Use Cases of Retrievers:
Chatbots: Enhancing responses with real-world or proprietary knowledge.
Question Answering Systems: Providing factual accuracy by referencing stored knowledge.
Enterprise AI Solutions: Connecting with databases, vector stores, and APIs to fetch data.
Why Other Options Are Incorrect:
(A) is incorrect because breaking tasks into smaller steps is handled by agents or chains.
(C) is incorrect because retrievers do not train LLMs; they enhance query responses.
(D) is incorrect because pipelines integrate components, whereas retrievers fetch external data.
🔹 Oracle Generative AI Reference:
Oracle AI integrates retrieval mechanisms in enterprise AI solutions, improving data-driven AI responses.

NEW QUESTION # 20
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token will be the only one considered in the next generation step.
B. The token is more likely to follow the current token.
C. The token is less likely to follow the current token.
D. The token is unrelated to the current token and will not be used.

Answer: B

NEW QUESTION # 21
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
B. PEFT parameters and b typically used when no training data exists.
C. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
D. PEFT involves only a few or new parameters and uses labeled, task-specific data.

Answer: D

NEW QUESTION # 22
What is LangChain?

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

Answer: A

Explanation:
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.

NEW QUESTION # 23
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

A. Serves as a designated point for user requests and model responses
B. Hosts the training data for fine-tuning custom model
C. Evaluates the performance metrics of the custom model
D. Updates the weights of the base model during the fine-tuning process

Answer: B

NEW QUESTION # 24
......

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