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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?

A) Ensure an equal distribution of positive, negative, and neutral sentiment examples
B) Increase the number of positive sentiment examples in the dataset
C) Focus only on neutral and negative examples to challenge the model
D) Convert neutral examples into either positive or negative to simplify the task

2. You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A) Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
B) Include fairness metrics in the model evaluation stage to monitor for biased outputs.
C) Use a comprehensive training dataset that includes diverse business domains to reduce biases.
D) Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.

3. You are designing a prompt to converse with a model for multilingual translation.
How would you frame the prompt to translate an English business email to Japanese, ensuring that the translated email is formal and appropriate for a business setting in Japan?

A) "Translate this business email into Japanese using informal language."
B) "Translate this business email into Japanese, making sure the tone is formal and culturally appropriate for a business context."
C) "Translate the following business email into Japanese, focusing on word-for-word accuracy."
D) "Translate this business email into Japanese but do not consider the tone or formalities."

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

5. In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A) Initial testing on synthetic datasets and model validation
B) Iterative prompt tuning based on A/B test results and feedback loops
C) Retraining the model based on emerging trends in data
D) Deployment to production with regular monitoring and logging

Solutions: