How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

• A. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
• B. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
• C. Both involve retraining the model, but Prompt Engineering does it more often.
• D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

解説: (Fast2test メンバーにのみ表示されます)

Which feature of OCI Speech helps make transcriptions easier to read and understand?

• A. Timestamping
• B. Text normalization
• C. Audio tuning
• D. Profanity filtering

解説: (Fast2test メンバーにのみ表示されます)

Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

• A. Unsupervised learning
• B. Reinforcement learning
• C. Active learning
• D. Supervised learning

解説: (Fast2test メンバーにのみ表示されます)

How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

• A. They focus on increasing the number of tokens while keeping the model size constant.
• B. They prioritize larger model sizes to achieve better performance.
• C. They disregard model size and prioritize high-quality data only.
• D. They ensure that the model size, training time, and data size are balanced for optimal results.

解説: (Fast2test メンバーにのみ表示されます)

Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

• A. Random Forest
• B. Gradient Descent
• C. Backpropagation
• D. Support Vector Machine

解説: (Fast2test メンバーにのみ表示されます)