1z0-1127-24 無料問題集「Oracle Cloud Infrastructure 2024 Generative AI Professional」

（A）To break down complex tasks into smaller steps

（B）To train Large Language Models

（C）To retrieve relevant information from knowledge bases

（D）To combine multiple components into a single pipeline

（A）It uses simple row-based data storage.

（B）It is not optimized for high-dimensional spaces.

（C）A vector database stores data in a linear or tabular format.

（D）It is based on distances and similarities in a vector space.

（A）To convert vectors into a nonindexed format for easier retrieval

（B）To map vectors to a data structure for faster searching, enabling efficient retrieval

（C）To compress vector data for minimized storage usage

（D）To categorize vectors based on their originating data type (text, images, audio)

（A）T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

（B）T- Few fine-tuning involves updating the weights of all layers in the model.

（C）T-Few fine-tuning requires manual annotation of input-output pain.

（D）T-Few fine-tuning relies on unsupervised learning techniques for annotation.

（A）To analyze the reasoning process of language

（B）To generate test cases for language models

（C）To monitor the performance of language models

（D）To debug issues in language model outputs

（A）It com rob the randomness of the model* output, affecting its creativity.

（B）It assigns a penalty to frequently occurring tokens to reduce repetitive text.

（C）It specifies a string that tells the model to stop generating more content

（D）It determines the maximum number of tokens the model can generate per response.

（A）Loss measures the total number of predictions made by a model.

（B）Loss is a measure that indicates how wrong the model's predictions are.

（C）Loss indicates how good a prediction is, and it should increase as the model improves.

（D）Loss describes the accuracy of the right predictions rather than the incorrect ones.