A. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

B. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.

C. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.

D. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.

A. To generate test cases for language models

B. To analyze the reasoning process of language models

C. To debug issues in language model outputs

D. To monitor the performance of language models

A. GPUs are shared with other customers to maximize resource utilization.

B. The GPUs allocated for a customer??s generative AI tasks are isolated from other GPUs.

C. GPUs are used exclusively for storing large datasets, not for computation.

D. Each customer's GPUs are connected via a public Internet network for ease of access.

A. It ensures that all vectors represent keywords only.

B. It significantly reduces the size of the database.

C. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

D. It converts all sparse vectors to dense vectors.

A. By the complexity of language used in the documents

B. Based on the number of images and videos contained in the documents

C. Based on the presence and frequency of the user-provided keywords

D. According to the length of the documents

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

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

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

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