Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

• A. Evaluates the performance metrics of the custom model
• B. Serves as a designated point for user requests and model responses
• C. Updates the weights of the base model during the fine-tuning process
• D. Hosts the training data for fine-tuning custom model

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

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

• A. 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.
• B. 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.
• C. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
• 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.

Given the following code: chain = prompt |11m

• A. LCEL is a legacy method for creating chains in LangChain
• B. LCEL is a declarative and preferred way to compose chains together.
• C. LCEL is a programming language used to write documentation for LangChain.
• D. Which statement is true about LangChain Expression language (ICED?

What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

• A. The level of incorrectness in the models predictions, with lower values indicating better performance
• B. The improvement in accuracy achieved by the model during training on the user-uploaded data set
• C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
• D. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model