先試後買

A. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

B. Both involve retraining the model, but Prompt Engineering does it more often.

C. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.

D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.

A. They are primarily used for image recognition tasks.

B. They have a feedback loop that allows information to persist across different time steps.

C. They do not have an internal state.

D. They process data in parallel.

A. Learning from outcomes to make decisions

B. Finding relationships within data sets

C. Making predictions from labeled data

D. Identifying patterns in data

A. Manage database security protocols.

B. Query data based on keywords.

C. Store business data in a cloud database.

D. Query data based on semantics.

A. They optimize the use of computational resources.

B. They facilitate semantic searches.

C. They enable creating detailed graphics.

D. They simplify managing databases.