Free Oracle 1Z0-1127-24 Exam Actual Questions

The questions for 1Z0-1127-24 were last updated On Apr 1, 2025

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Question No. 1

Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

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Correct Answer: D

The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.

Reference

Technical documentation on T-Few fine-tuning

Research articles comparing fine-tuning methods in machine learning


Question No. 2

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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Correct Answer: A

Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.

Reference

OCI documentation on data storage and security practices

Technical details on encryption and data privacy in OCI services


Question No. 3

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

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Correct Answer: B

Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.

Reference

Research articles on Chain-of-Thought prompting

Technical guides on enhancing model transparency and reasoning with intermediate steps


Question No. 4

How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

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Correct Answer: D

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.

Reference

Research papers on T-Few fine-tuning techniques

Technical guides on optimizing transformer models


Question No. 5

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?

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Correct Answer: A

An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.

Reference

Research papers on diffusion models and their applications

Technical documentation on generative models for image and text synthesis