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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	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.
Topic 3	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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

A. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
B. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
C. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
D. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.

Answer: C

NEW QUESTION # 50
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Translation models
B. Embedding models
C. Summarization models
D. Generation models

Answer: A

NEW QUESTION # 51
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Increased model interpretability
B. Enhanced generalization to unseen data
C. Reduced model complexity
D. Foster training time and lower cost

Answer: D

Explanation:
The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.
Reference
Technical documentation on T-Few fine-tuning
Research articles comparing fine-tuning methods in machine learning

NEW QUESTION # 52
What is LangChain?

A. A Python library for building applications with Large Language Models
B. A JavaScript library for natural language processing
C. A Java library for text summarization
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 # 53
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It limits the number of fine-tuned model deployable on the same GPU cluster.
B. It increases G PU memory requirements for model deployment.
C. It leads to higher latency in model inference.
D. It enables the deployment of multiple fine-tuned models.

Answer: D

NEW QUESTION # 54
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Oracle Cloud Infrastructure 1z0-1127-24 free valid dumps & Oracle 1z0-1127-24 actual pdf exam

Oracle 1z0-1127-24 Exam Syllabus Topics:

Pass Guaranteed Quiz 2026 Oracle 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Fantastic Valid Test Practice

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q49-Q54):