Online Access Free 1z0-1127-24 Practice Test

What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

• A.It specifies a string that tells the model to stop generating more content
• B.It assigns a penalty to frequently occurring tokens to reduce repetitive text.
• C.It com rob the randomness of the model* output, affecting its creativity.
• D.It determines the maximum number of tokens the model can generate per response.

Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

• A.Temporal relationships; necessary for predicting future linguistic trends
• B.Semantic relationships; crucial for understanding context and generating precise language
• C.Linear relationships; they simplify the modeling process
• D.Hierarchical relationships; important for structuring database queries

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

• A.Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
• B.Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
• C.PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
• D.Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.

Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

• A.Foster training time and lower cost
• B.Reduced model complexity
• C.Increased model interpretability
• D.Enhanced generalization to unseen 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 croup of transformer Layers
• B.By allowing updates across all layers of the model
• C.By incorporating additional layers to the base model
• D.By excluding transformer layers from the fine-tuning process entirely