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1. You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?

A) Deploying the model in a real-time environment for user feedback collection.
B) Pre-processing and augmenting the training data to improve the model's generalization capabilities.
C) Running evaluation metrics on the baseline model to measure initial performance.
D) Defining task-specific instructions and fine-tuning them through prompt design.

2. You are optimizing a generative AI prompt for creative content generation, but you want to ensure that outputs with the same prompt can vary slightly across multiple runs to maintain freshness in the responses.
Which parameter should you adjust to strike a balance between random variability and consistent quality?

A) Temperature = 0.1, Random Seed unset
B) Top-p = 0.9, Temperature = 1.0, Random Seed unset
C) Max Tokens = 300, Random Seed set to 42
D) Set a fixed Random Seed to zero

3. After prompt-tuning a generative AI model, you review its performance on multiple evaluation metrics. The metrics include accuracy, perplexity, and latency.
Which combination of these metrics would most effectively allow you to optimize the model for both user experience and content quality?

A) High accuracy, high perplexity, high latency
B) High accuracy, low perplexity, high latency
C) Low accuracy, high perplexity, low latency
D) High accuracy, low perplexity, low latency

4. When using Tuning Studio in IBM watsonx, which of the following types of models is most suitable for fine-tuning in a domain-specific task to optimize performance?

A) A pre-trained model with very few layers to ensure fast processing speeds.
B) A model trained from scratch on a small, specific dataset.
C) An untrained model with a minimal number of layers to reduce complexity.
D) A pre-trained model that has been trained on a large, general-purpose dataset.

5. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Increase the length of the input prompt to ensure that responses are more complete.
B) Lower the model's perplexity score to improve both completeness and factual accuracy.
C) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
D) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

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