1z0-1127-24 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

The Retrieval-Augmented Generation (RAG) technique enhances the response generation process of language models by incorporating relevant external documents. RAG Token and RAG Sequence are two variations of this technique.

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally. This means that during the response generation process, the model continuously retrieves and incorporates information from external documents as it generates each token (or part) of the response. This allows for more dynamic and contextually relevant answers, as the model can adjust its retrieval based on the evolving context of the response.

In contrast, RAG Sequence typically retrieves documents once at the beginning of the response generation and uses those documents to generate the entire response. This approach is less dynamic compared to RAG Token, as it does not adjust the retrieval process during the generation of the response.

References

Research articles on Retrieval-Augmented Generation (RAG) techniques

Documentation on advanced language model inference methods

Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.

How RAG Works:

The model retrieves relevant documents from an external database.

Uses this retrieved information to generate factually grounded responses.

Reduces hallucinations, improving accuracy and context relevance.

Why Other Options Are Incorrect:

(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.

(B) is incorrect because RAG retrieves and uses data, not just stores it.

(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.

???? Oracle Generative AI Reference:Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.

The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.

References

Research papers on nucleus sampling and token selection methods

OCI Generative AI model documentation

The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.

References

Research papers on T-Few fine-tuning techniques

Technical guides on optimizing transformer models

The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.

References

OCI Generative AI service documentation

General principles of sequence generation in AI models

Accuracy in machine learning measures the proportion of correct predictions made by a model relative to the total predictions during an evaluation.

How Accuracy is Calculated:

A black text with a black line AI-generated content may be incorrect.

A higher accuracy indicates better model performance.

Used primarily in classification tasks, but it can also assess LLM fine-tuning results.

Why Other Options Are Incorrect:

(A) is incorrect because the number of neural network layers does not define accuracy.

(B) is incorrect because accuracy considers correctness, not just total predictions.

(D) is incorrect because accuracy measures correct predictions, not just incorrect ones.

???? Oracle Generative AI Reference:Oracle AI assesses model fine-tuning performance using accuracy, loss, and perplexity to improve LLM capabilities.

Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.

References

Research papers on vector normalization in information retrieval

Technical documentation on hybrid search systems

Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.

References

Research papers on Parameter-Efficient Fine-Tuning (PEFT)

Technical documentation on fine-tuning techniques for large language models

In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.

References

Research papers on RAG (Retrieval-Augmented Generation)

Technical documentation on the architecture of RAG models

The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.

References

Oracle ADS documentation

Guides on deploying AI models using Oracle Data Science services