1Z0-1127-24 VALID TEST MATERIALS & 1Z0-1127-24 TRUSTWORTHY DUMPS

1z0-1127-24 Valid Test Materials & 1z0-1127-24 Trustworthy Dumps

1z0-1127-24 Valid Test Materials & 1z0-1127-24 Trustworthy Dumps

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
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 (Q65-Q70):

NEW QUESTION # 65
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Improved retrievals for Retrieval Augmented Generation (RAG) systems
  • B. Emphasis on syntactic clustering of word embedding's
  • C. Capacity to translate text in over u languages
  • D. Support for tokenizing longer sentences

Answer: A

Explanation:
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems


NEW QUESTION # 66
What is LangChain?

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

Answer: C

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 # 67
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


NEW QUESTION # 68
Why is normalization of vectors important before indexing in a hybrid search system?

  • A. It significantly reduces the size of the database.
  • B. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
  • C. It ensures that all vectors represent keywords only.
  • D. It converts all sparse vectors to dense vectors.

Answer: B


NEW QUESTION # 69
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

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

Answer: A


NEW QUESTION # 70
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