Oracle Cloud Infrastructure 2025 Generative AI Professional - 1Z0-1127-25 FREE EXAM DUMPS QUESTIONS & ANSWERS

A. "Top p" assigns penalties to frequently occurring tokens. B. "Top p" selects tokens from the "Top k" tokens sorted by probability. C. "Top p" limits token selection based on the sum of their probabilities. D. "Top p" determines the maximum number of tokens per response.

A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance. B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity. C. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity. D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

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

A. It significantly reduces the latency for each model request. B. It allows the LLM to access a larger dataset. C. It eliminates the need for any training or computational resources. D. It provides examples in the prompt to guide the LLM to better performance with no training cost.

A. It penalizes a token each time it appears after the first occurrence. B. It penalizes all tokens equally, regardless of how often they have appeared. C. It only penalizes tokens that have never appeared in the text before. D. It applies a penalty only if the token has appeared more than twice.

A. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training B. When the model requires continued pretraining on unlabeled data C. When the model needs to be adapted to perform well in a domain on which it was not originally trained D. When there is a significant amount of labeled, task-specific data available

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. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data. C. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data. D. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

A. Adjusting the hyperparameters of the model B. Iteratively refining the ask to elicit a desired response C. Adding more layers to the neural network D. Training the model on a large dataset

A. They cannot generate responses without fine-tuning. B. They use vector databases exclusively to produce answers. C. They rely on internal knowledge learned during pretraining on a large text corpus. D. They always use an external database for generating responses.