1Z0-1122-25 無料問題集「Oracle Cloud Infrastructure 2025 AI Foundations Associate」

（A）Extract text from documents

（B）Extract tables from documents

（C）Generate transcript from documents

（D）Classify documents into different types

（A）Provide a mechanism to process sequential data in parallel and capture long-range dependencies

（B）Limit the ability of LLMs to handle large datasets by imposing strict memory constraints

（C）Image recognition tasks in LLMs

（D）Manually engineer features in the data before training the model

（A）Chat models

（B）Translation models

（C）Embedding models

（D）Generation models

（A）Assigning classification labels to images

（B）Translating text in images to another language

（C）Object detection with bounding boxes

（D）Locating and extracting text in images

（A）Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

（B）Both involve retraining the model, but Prompt Engineering does it more often.

（C）Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.

（D）Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.