1z0-1127-24 試験問題を無料オンラインアクセス

Which statement best describes the role of encoder and decoder models in natural language processing?

• A.Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
• B.Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
• C.Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
• D.Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

Which is NOT a built-in memory type in LangChain?

• A.Conversation Buffer Memory
• B.Conversation Token Buffer Memory
• C.Conversation ImgeMemory
• D.Conversation Summary Memory

Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

• A.PEFT modifies all parameters and uses unlabeled, task-agnostic data.
• B.PEFT involves only a few or new parameters and uses labeled, task-specific data.
• C.PEFT parameters and b typically used when no training data exists.
• D.PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies

Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

• A.They require frequent manual updates, which increase operational costs.
• B.They are more expensive but provide higher quality data.
• C.They increase the cost due to the need for real- time updates.
• D.They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.

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

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