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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A financial services company is building a generative AI model to assist with customer support. The company is concerned about potential legal liabilities if the model generates customer information, such as bank account numbers or personal identification data, as part of its responses.
Which of the following techniques would best mitigate the risk of generating Personally Identifiable Information (PII) during inference?

A) Set a strict token limit to prevent the model from generating long sequences, assuming PII tends to appear in longer outputs.
B) Train the model on sensitive customer data but ensure that the temperature is set low to avoid generating diverse outputs.
C) Use greedy decoding to ensure the model generates only the most probable tokens, which are less likely to include PII.
D) Implement a real-time PII filter that detects and removes sensitive data before the output is presented to the user.

2. 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) Use adversarial debiasing techniques to adjust the model's internal representations during training.
C) Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
D) Limit the model's context window to prevent it from over-relying on demographic information.

3. You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?

A) Restrict the model's output length to reduce the risk of sensitive information leakage.
B) Increase the length of the prompts to provide more context, ensuring more accurate results.
C) Employ differential privacy techniques to add noise to the model's outputs.
D) Utilize a smaller model to minimize the likelihood of overfitting sensitive data.

4. You are tuning a generative AI model to reduce repetitive outputs, which often occur when generating long texts.
Which of the following parameter adjustments would most likely reduce the model's tendency to repeat words or phrases without compromising the quality of the generated text?

A) Increasing the repetition penalty to 1.2
B) Lowering the temperature to 0.1
C) Increasing the Top-k value to 200
D) Reducing the beam size to 1

5. You are selecting a model to fine-tune using Tuning Studio for a financial application that requires high accuracy and domain-specific language understanding.
Which type of model should you select to maximize fine-tuning efficiency and performance?

A) A pre-trained model that has been optimized for creative text generation.
B) A large pre-trained language model that has been fine-tuned on generic business communication.
C) A model pre-trained on financial and business data but with limited language capabilities.
D) A small pre-trained model specifically designed for open-domain tasks.

Solutions: