IBM C1000-185 : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are working with a healthcare provider to deploy a generative AI model that assists in diagnosing patient conditions based on medical history and symptoms. The healthcare provider is concerned about ethical use, bias, and compliance with health data regulations such as HIPAA.
Which governance approach is the most critical to ensure both compliance and ethical AI deployment?

A. Ensure data encryption during model inference to protect patient information from unauthorized access.
B. Implement a bias detection framework to identify and mitigate any biases present in the model's outputs.
C. Use a cloud-based infrastructure with HIPAA certification to deploy the model, ensuring compliance with healthcare regulations.
D. Apply a federated learning approach to train the model without directly accessing the healthcare provider's patient data.

Question 2

After prompt-tuning a language model, you notice that certain outputs are semantically correct but syntactically flawed.
Which of the following actions is most appropriate to resolve this issue and optimize the tuned model's performance?

A. Use a higher temperature during the generation process
B. Increase the model's training dataset size
C. Fine-tune the prompt template to emphasize grammar
D. Lower the learning rate during the tuning phase

Question 3

After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A. Low validation loss
B. High training accuracy
C. Low training loss
D. Large difference between training and validation accuracy
E. Small difference between training and validation loss

Question 4

You are optimizing a large language model (LLM) by prompt-tuning it for specific enterprise-level tasks. The goal is to initialize the prompt in such a way that it helps the model generalize well across various enterprise domains, such as finance, healthcare, and retail.
What is the most effective method to initialize the prompt for such a use case?

A. Start with a general prompt and gradually specialize it during fine-tuning
B. Use a short prompt that provides no guidance and allow the model to self-optimize
C. Initialize the prompt with an ensemble of prompts covering multiple domains
D. Use a single, highly specific prompt tailored to only one domain, such as finance

Question 5

When conducting prompt engineering to reduce model risks related to hate speech and abusive content, which of the following strategies is least likely to be effective?

A. Increasing the temperature during text generation to encourage more creative outputs
B. Applying ethical guidelines during the fine-tuning of the model to prioritize inclusive language
C. Modifying the prompt to include explicit instructions for civil language
D. Creating a reward model during Reinforcement Learning to penalize hate speech outputs

Solutions: