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1. You are deploying a new version of a generative AI model in IBM Watsonx, and you want to maintain the integrity of prompt versioning throughout the deployment lifecycle.
Which of the following methods is the most effective for ensuring that the correct prompt version is used with the corresponding model version in production?

A) Use semantic versioning for both the model and the associated prompts, and track them independently in separate systems.
B) Implement model registry tags that associate a specific prompt version with each model version during deployment.
C) Use the latest available prompt version for every deployment, without specifying an exact version
D) Hard-code the prompt within the model deployment script to ensure that the correct prompt is always used

2. You are deploying a generative AI model for a financial services company. The model is responsible for automating customer support and providing recommendations. Due to the sensitive nature of financial data, the company emphasizes the need for robust AI governance.
What governance mechanism should you prioritize to ensure compliance with data privacy regulations and maintain trust in AI outputs?

A) Regularly retraining the model to avoid performance degradation due to data drift.
B) Using AI explainability techniques to make the model's decisions transparent to regulators and customers.
C) Ensuring model version control to track changes and updates made to the model during the deployment process.
D) Implementing role-based access control (RBAC) to restrict who can interact with the model.

3. You are fine-tuning a generative model to generate text-based responses in a customer service chatbot. You want to ensure the responses are concise and relevant, without causing the model to produce overly long or irrelevant output.
Which of the following parameters and stopping criteria would be most effective for achieving this goal?

A) Use greedy decoding with no repetition penalty and a high stopping probability threshold.
B) Increase the temperature to 1.5 and set a high maximum token limit.
C) Set a low top-k value and implement a repetition penalty with a low maximum token limit.
D) Use beam search decoding with a low beam width and a high repetition penalty.

4. Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A) Hybrid quantization
B) Post-training static quantization
C) Quantization-aware training (QAT)
D) Post-training dynamic quantization

5. You are working with IBM Watsonx and need to generate synthetic data to improve your model's performance on a custom domain-specific task. After importing a dataset, you want to use the User Interface to generate this synthetic data.
What is the primary benefit of using synthetic data generation in fine-tuning your model?

A) It automatically anonymizes sensitive data points to comply with data privacy regulations during the synthetic data generation process.
B) It eliminates the need for any human intervention in the fine-tuning process.
C) It improves the model's generalization by exposing it to a wider variety of data points and scenarios.
D) It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy.

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