1Z0-1127-25 試験問題を無料オンラインアクセス

What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

• A.Providing the exact k words in the prompt to guide the model's response
• B.Limiting the model to only k possible outcomes or answers for a given task
• C.The process of training the model on k different tasks simultaneously to improve its versatility
• D.Explicitly providing k examples of the intended task in the prompt to guide the model's output

What differentiates Semantic search from traditional keyword search?

• A.It is based on the date and author of the content.
• B.It relies solely on matching exact keywords in the content.
• C.It involves understanding the intent and context of the search.
• D.It depends on the number of times keywords appear in the content.

Why is normalization of vectors important before indexing in a hybrid search system?

• A.It significantly reduces the size of the database.
• B.It ensures that all vectors represent keywords only.
• C.It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
• D.It converts all sparse vectors to dense vectors.

Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

• A.Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
• B.Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
• C.Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
• D.Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.

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

• A.By restricting updates to only a specific group of transformer layers
• B.By incorporating additional layers to the base model
• C.By excluding transformer layers from the fine-tuning process entirely
• D.By allowing updates across all layers of the model