최신Oracle Cloud Infrastructure 2024 Generative AI Professional - 1z0-1127-24무료샘플문제

A. Loss indicates how good a prediction is, and it should increase as the model improves.

B. Loss is a measure that indicates how wrong the model's predictions are.

C. Loss describes the accuracy of the right predictions rather than the incorrect ones.

D. Loss measures the total number of predictions made by a model.

A. Detecting bias or toxicity

B. Evaluating factual accuracy of outputs

C. Aliening code readability

D. Measuring coherence of generated text

A. T-Few fine-tuning requires manual annotation of input-output pain.

B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

C. T- Few fine-tuning involves updating the weights of all layers in the model.

D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.

A. Conversation Summary Memory

B. Conversation Token Buffer Memory

C. Conversation ImgeMemory

D. Conversation Buffer Memory

A. Generation models

B. Embedding models

C. Translation models

D. Summarization models

A. Top p limits token selection based on the sum of their probabilities.

B. Top p selects tokens from the "Top k' tokens sorted by probability.

C. Top p assigns penalties to frequently occurring tokens.

D. Top p determines the maximum number of tokens per response.

A. It determines the maximum number of tokens the model can generate per response.

B. It com rob the randomness of the model* output, affecting its creativity.

C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.

D. It specifies a string that tells the model to stop generating more content