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Oracle Cloud Infrastructure 2024 Generative AI Professional best valid exam torrent & 1z0-1127-24 useful brain dumps

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Posted on: 05/29/25

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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 (Q52-Q57):

NEW QUESTION # 52
What does the RAG Sequence model do in the context of generating a response?

  • A. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
  • B. It modifies the input query before retrieving relevant documents to ensure a diverse response.
  • C. It retrieves relevant documents only for the initial part of the query and ignores the rest.
  • D. It retrieves a single relevant document for the entire input query and generates a response based on that alone.

Answer: A

Explanation:
RAG (Retrieval-Augmented Generation) Sequence models combine retrieval-based search with LLM-generated responses, ensuring factually grounded and contextually relevant outputs.
How the RAG Sequence Model Works:
Retrieves multiple documents for an input query.
Uses all retrieved documents collectively to generate a well-informed response.
Ensures the answer is contextually aware and factually accurate.
Why Other Options Are Incorrect:
(A) is incorrect because RAG does not ignore part of the query.
(B) is incorrect because it does not rely on a single document.
(C) is incorrect because RAG does not modify the input query but focuses on retrieval and generation.
🔹 Oracle Generative AI Reference:
Oracle AI implements RAG-based architectures to enhance LLM-generated responses by retrieving and grounding responses in factual data.


NEW QUESTION # 53
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • B. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
  • C. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
  • D. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Answer: C

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures


NEW QUESTION # 54
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It requires a large temperature setting to ensure diverse word selection.
  • B. It selects words bated on a flattened distribution over the vocabulary.
  • C. It picks the most likely word email at each step of decoding.
  • D. It chooses words randomly from the set of less probable candidates.

Answer: C


NEW QUESTION # 55
When should you use the T-Few fine-tuning method for training a model?

  • A. For complicated semantical undemanding improvement
  • B. For data sets with hundreds of thousands to millions of samples
  • C. For models that require their own hosting dedicated Al duster
  • D. For data sets with a few thousand samples or less

Answer: B


NEW QUESTION # 56
What is LangChain?

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

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 # 57
......

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