AUTHORITATIVE EXAMCOLLECTION 1Z0-1127-24 QUESTIONS ANSWERS - 100% PASS 1Z0-1127-24 EXAM

Authoritative Examcollection 1z0-1127-24 Questions Answers - 100% Pass 1z0-1127-24 Exam

Authoritative Examcollection 1z0-1127-24 Questions Answers - 100% Pass 1z0-1127-24 Exam

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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 (Q62-Q67):

NEW QUESTION # 62
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 # 63
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. In context Learning
  • D. Step-Bock Prompting

Answer: A


NEW QUESTION # 64
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

  • A. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
  • B. They require frequent manual updates, which increase operational costs.
  • C. They increase the cost due to the need for real- time updates.
  • D. They are more expensive but provide higher quality data.

Answer: A

Explanation:
Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamic retrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.
Reference
Articles on the cost efficiency of vector databases
Research on integrating vector databases with LLMs for real-time updates


NEW QUESTION # 65
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

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

Answer: A

Explanation:
In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI


NEW QUESTION # 66
Why is it challenging to apply diffusion models to text generation?

  • A. Because text generation does not require complex models
  • B. Because diffusion models can only produce images
  • C. Because text is not categorical
  • D. Because text representation is categorical unlike images

Answer: D

Explanation:
Diffusion models are primarily used for image generation because they work by incrementally adding noise to a data distribution and then learning to remove it, effectively denoising an image over time. This method works well for continuous data, such as pixel values in images.
However, text is fundamentally categorical, meaning:
Discrete Nature of Text - Unlike images where pixel values change smoothly, text is composed of discrete symbols (words, characters, or tokens), making it difficult to apply continuous noise diffusion.
Tokenization Challenges - Language models work with tokenized words or subwords. Diffusion models would need a way to gradually transition between discrete text tokens, which is not straightforward.
Non-Sequential Nature of Noise Addition - Image-based diffusion models can modify pixel values slightly to learn transformations, but text does not have an equivalent smooth transformation between words.
Alternative Approaches in Text Generation - Due to these challenges, text generation relies more on transformer-based models (like Oracle's AI-driven NLP models), which handle categorical text more effectively than diffusion methods.
???? Oracle Generative AI Reference:
Oracle focuses on transformer-based models for text-related AI applications rather than diffusion models, as transformers are more effective in understanding and generating text.


NEW QUESTION # 67
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