1Z0-1127-25 無料問題集「Oracle Cloud Infrastructure 2025 Generative AI Professional」

（A）"Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.

（B）"Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.

（C）"Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

（D）"Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.

（A）It involves understanding the intent and context of the search.

（B）It depends on the number of times keywords appear in the content.

（C）It is based on the date and author of the content.

（D）It relies solely on matching exact keywords in the content.

（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 pairs.

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

（A）The model ignores periods and continues generating text until it reaches the token limit.

（B）The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.

（C）The model generates additional sentences to complete the paragraph.

（D）The model stops generating text after it reaches the end of the current paragraph.

（A）Summarization models

（B）Translation models

（C）Embedding models

（D）Generation models

（A）It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.

（B）It increases the training time as compared to Vanilla fine-tuning.

（C）It updates all the weights of the model uniformly.

（D）It selectively updates only a fraction of weights to reduce the number of parameters.

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

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

（C）It stores data in a linear or tabular format.

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

（A）It does not update any weights but restructures the model architecture.

（B）It selectively updates only a fraction of the model's weights.

（C）It increases the training time as compared to Vanilla fine-tuning.

（D）It updates all the weights of the model uniformly.

（A）It modifies the input query before retrieving relevant documents to ensure a diverse response.

（B）It retrieves a single relevant document for the entire input query and generates a response based on that alone.

（C）It retrieves relevant documents only for the initial part of the query and ignores the rest.

（D）For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.