TRUSTWORTHY ORACLE 1Z0-1127-24 SOURCE, 1Z0-1127-24 PRACTICE TEST PDF

Trustworthy Oracle 1z0-1127-24 Source, 1z0-1127-24 Practice Test Pdf

Trustworthy Oracle 1z0-1127-24 Source, 1z0-1127-24 Practice Test Pdf

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

  • A. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
  • B. It enables them to bypass the need for pretraining on large text corpora.
  • C. It transforms their architecture from a neural network to a traditional database system.
  • D. It limits their ability to understand and generate natural language.

Answer: A

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs


NEW QUESTION # 38
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

  • A. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
  • B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
  • C. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
  • D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Answer: A


NEW QUESTION # 39
When should you use the T-Few fine-tuning method for training a model?

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

Answer: A

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models


NEW QUESTION # 40
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They have the same magnitude
  • B. They are completely dissimilar
  • C. They are similar in direction
  • D. They are unrelated

Answer: C


NEW QUESTION # 41
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

  • A. Chain-of-Through
  • B. Least to most Prompting
  • C. Step-Bock Prompting
  • D. In context Learning

Answer: A


NEW QUESTION # 42
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