LATEST UPDATED ORACLE 1Z0-1127-24: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL RELIABLE EXAM PATTERN - RELIABLE PASS4SUREQUIZ DUMPS 1Z0-1127-24 QUESTIONS

Latest updated Oracle 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Reliable Exam Pattern - Reliable Pass4SureQuiz Dumps 1z0-1127-24 Questions

Latest updated Oracle 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Reliable Exam Pattern - Reliable Pass4SureQuiz Dumps 1z0-1127-24 Questions

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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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

  • A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • B. The level of incorrectness in the models predictions, with lower values indicating better performance
  • C. The improvement in accuracy achieved by the model during training on the user-uploaded data set
  • D. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

Answer: B

Explanation:
In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.
Reference
Articles on loss functions in machine learning
OCI Generative AI service documentation on model evaluation metrics


NEW QUESTION # 64
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

  • A. Updates the weights of the base model during the fine-tuning process
  • B. Hosts the training data for fine-tuning custom model
  • C. Serves as a designated point for user requests and model responses
  • D. Evaluates the performance metrics of the custom model

Answer: B


NEW QUESTION # 65
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

  • A. The token is more likely to follow the current token.
  • B. The token is unrelated to the current token and will not be used.
  • C. The token will be the only one considered in the next generation step.
  • D. The token is less likely to follow the current token.

Answer: A

Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models


NEW QUESTION # 66
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

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

Answer: C

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 # 67
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

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

Answer: B

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 # 68
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