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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?

A) Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.
B) Limit the model's context window to prevent it from over-relying on demographic information.
C) Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
D) Use adversarial debiasing techniques to adjust the model's internal representations during training.

2. When tuning the generative model parameters, which of the following scenarios describes an appropriate use of the maximum tokens setting, and how will it influence the model's output?

A) Setting the maximum tokens to 500 will guarantee that the model always generates exactly 500 tokens
B) Setting the maximum tokens to 0 will allow the model to generate an unlimited amount of text
C) Setting the maximum tokens to 100 ensures that the model's output will not exceed 100 tokens, but it may stop earlier if the generation ends naturally
D) Setting the maximum tokens to 100 will prevent the model from generating coherent sentences, as it cuts off abruptly after 100 tokens

3. You are using IBM Watsonx to generate answers for a customer service chatbot. However, the responses generated sometimes include fabricated details that are inaccurate (hallucinations).
Which of the following strategies will best mitigate the risk of hallucination when designing your prompt?

A) Use subjective language in your prompts: ":Can you describe why our product is the best on the market?"
B) Rely on the AI model to fact-check and correct hallucinations in its responses automatically: "What can you tell me about our product?"
C) Supply explicit product details and constraints in the prompt: "List the latest features of the XYZ smartphone released in 2024, including its AI-powered camera and battery-saving mode."
D) Ask open-ended questions without providing reference information: "What are the latest features of our product?"

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

5. You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A) Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
B) Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
C) Ensure that the synthetic data covers edge cases as well as common industry scenarios.
D) Generate only a small amount of synthetic data to minimize computational costs and training time.
E) Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.

Solutions: