TOP ORACLE 1Z0-1127-24 LATEST EXAM FORMAT & AUTHORITATIVE ACTUALTESTSQUIZ - LEADER IN CERTIFICATION EXAM MATERIALS

Top Oracle 1z0-1127-24 Latest Exam Format & Authoritative ActualTestsQuiz - Leader in Certification Exam Materials

Top Oracle 1z0-1127-24 Latest Exam Format & Authoritative ActualTestsQuiz - Leader in Certification Exam Materials

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The Oracle 1z0-1127-24 certification can play a crucial role in career advancement and increase your earning potential. By obtaining Oracle 1z0-1127-24 certification, you can demonstrate to employers your expertise and knowledge. The Oracle world is constantly changing its dynamics. With the Oracle 1z0-1127-24 Certification Exam you can learn these changes and stay updated with the latest technologies and trends.

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
  • 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.
Topic 3
  • 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
Given the following code: chain = prompt |11m

  • A. LCEL is a programming language used to write documentation for LangChain.
  • B. LCEL is a legacy method for creating chains in LangChain
  • C. Which statement is true about LangChain Expression language (ICED?
  • D. LCEL is a declarative and preferred way to compose chains together.

Answer: D

Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language


NEW QUESTION # 58
What does the Loss metric indicate about a model's predictions?

  • A. Loss is a measure that indicates how wrong the model's predictions are.
  • B. Loss measures the total number of predictions made by a model.
  • C. Loss describes the accuracy of the right predictions rather than the incorrect ones.
  • D. Loss indicates how good a prediction is, and it should increase as the model improves.

Answer: A

Explanation:
In machine learning and AI models, the loss metric quantifies the error between the model's predictions and the actual values.
Definition of Loss:
Loss represents how far off the model's predictions are from the expected output.
The objective of training an AI model is to minimize loss, improving its predictive accuracy.
Loss functions are critical in gradient descent optimization, which updates model parameters.
Types of Loss Functions:
Mean Squared Error (MSE) - Used for regression problems.
Cross-Entropy Loss - Used in classification problems (e.g., NLP tasks).
Hinge Loss - Used in Support Vector Machines (SVMs).
Negative Log-Likelihood (NLL) - Common in probabilistic models.
Clarifying Other Options:
(B) is incorrect because loss does not count the number of predictions.
(C) is incorrect because loss focuses on both right and wrong predictions.
(D) is incorrect because loss should decrease as a model improves, not increase.
???? Oracle Generative AI Reference:
Oracle AI platforms implement loss optimization techniques in their training pipelines for LLMs, classification models, and deep learning architectures.


NEW QUESTION # 59
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

  • A. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • B. A Large Language Model based agent that focuses on generating textual responses
  • C. A diffusion model that specializes in producing complex outputs.
  • D. A language model that operates on a token-by-token output basis

Answer: A


NEW QUESTION # 60
In LangChain, which retriever search type is used to balance between relevancy and diversity?

  • A. similarity
  • B. similarity_score_threshold
  • C. top k
  • D. mmr

Answer: A


NEW QUESTION # 61
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 improvement in accuracy achieved by the model during training on the user-uploaded data set
  • B. The level of incorrectness in the models predictions, with lower values indicating better performance
  • C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • 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 # 62
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