Oracle Cloud Infrastructure 2025 Generative AI Professional - 1Z0-1127-25무료 덤프문제 풀어보기

A. It selectively updates only a fraction of weights to reduce the number of parameters.

B. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.

C. It updates all the weights of the model uniformly.

D. It increases the training time as compared to Vanilla fine-tuning.

A. To randomly penalize some tokens to increase the diversity of the text

B. To reward the tokens that have never appeared in the text

C. To penalize tokens that have already appeared, based on the number of times they have been used

D. To ensure that tokens that appear frequently are used more often

A. The proportion of incorrect predictions made by the model during an evaluation

B. The number of predictions a model makes, regardless of whether they are correct or incorrect

C. The depth of the neural network layers used in the model

D. How many predictions the model made correctly out of all the predictions in an evaluation

A. Temporal relationships; necessary for predicting future linguistic trends

B. Semantic relationships; crucial for understanding context and generating precise language

C. Hierarchical relationships; important for structuring database queries

D. Linear relationships; they simplify the modeling process

A. A Retrieval Augmented Generation (RAG) model that uses text as input and output

B. A Large Language Model-based agent that focuses on generating textual responses

C. A diffusion model that specializes in producing complex outputs.

D. A language model that operates on a token-by-token output basis

A. 25 unit hours

B. 40 unit hours

C. 30 unit hours

D. 20 unit hours

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

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

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

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

A. Adjusting the hyperparameters of the model

B. Adding more layers to the neural network

C. Iteratively refining the ask to elicit a desired response

D. Training the model on a large dataset

A. Increasing the temperature flattens the distribution, allowing for more varied word choices.

B. Decreasing the temperature broadens the distribution, making less likely words more probable.

C. Increasing the temperature removes the impact of the most likely word.

D. Temperature has no effect on probability distribution; it only changes the speed of decoding.