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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are designing a generative AI model to generate detailed technical

A) Stop Sequence = '<END>', Max Tokens = 100
B) Max Tokens = 500: This provides enough space for a detailed technical
C) Stop Sequence = 'END', Max Tokens = 500
D) Stop Sequence = None, Max Tokens = 700

2. You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

A) BLEU score
B) Log-likelihood of generated responses
C) Token generation speed
D) Perplexity score

3. You are tasked with preparing a dataset for training a machine learning model using IBM Watsonx. The dataset contains over 1 million rows, and you notice a significant imbalance in the distribution of class labels.
To optimize model performance and minimize bias, what would be the best next step in addressing this imbalance?

A) Randomly shuffle the data to improve the model's exposure to different instances during training.
B) Increase the learning rate of the model to improve its ability to learn from the imbalanced data.
C) Remove the majority class instances to balance the dataset.
D) Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic data for the minority class.

4. You are tasked with creating a prompt template for IBM Watsonx that will help generate product reviews for a new line of smartphones. The prompt needs to be adaptable across various product features and sentiment (positive, neutral, or negative) while maintaining a consistent structure.
Which of the following prompt templates would be the most effective for generating well-structured, detailed reviews?

A) "Write a [sentiment] review of the [product name], focusing on the [specific feature]. Include reasons for the sentiment and detailed examples."
B) "Tell a story about the user's experience with the smartphone."
C) ":Generate a review mentioning the best features of the smartphone."
D) "Write a review about the smartphone."

5. You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

A) The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.
B) The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
C) The model is trained on open-source financial data, which results in slower response times during inference.
D) The model occasionally generates offensive or inappropriate content when responding to user queries.
E) The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.

Solutions: