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1. You are fine-tuning a generative AI model using Tuning Studio for a legal document analysis task. The model needs to perform well in summarizing long, complex legal texts while minimizing the amount of computational resources used. You are tasked with optimizing the tuning process to ensure maximum efficiency and model accuracy.
Which of the following actions would most effectively optimize the tuning process in Tuning Studio for this task?

A) Adjusting the learning rate dynamically during training based on the model's real-time loss values.
B) Using a fixed number of training epochs to ensure consistent results across tuning iterations.
C) Increasing the batch size to reduce the number of gradient updates during training, thus speeding up the process.
D) Reducing the number of layers in the model to decrease computational overhead.

2. You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?

A) Use a higher learning rate to improve model convergence
B) Analyze token distribution patterns in the generated outputs
C) Increase the number of training epochs to further refine the model
D) Apply model quantization to reduce the complexity of the output

3. After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A) Low training loss
B) Small difference between training and validation loss
C) Low validation loss
D) Large difference between training and validation accuracy
E) High training accuracy

4. You are tasked with fine-tuning a large language model (LLM) to perform sentiment analysis on product reviews. The dataset contains customer reviews, but some reviews are very short, and others contain irrelevant data like product specifications or spam. You want to prepare the dataset for fine-tuning by ensuring the data is clean, relevant, and representative of the task at hand.
Which of the following steps is most critical to ensure the dataset is suitable for fine-tuning?

A) Increase the learning rate to adjust for inconsistencies in the dataset
B) Randomly remove 10% of the dataset to reduce noise
C) Increase the number of epochs to handle the noisy data
D) Filter the dataset to remove irrelevant or outlier reviews

5. When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

A) Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.
B) Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.
C) Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.
D) Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.

問題與答案：