IBM C1000-185

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)

A) Apply a low top-k value (e.g., k=10) to restrict randomness
B) Set a high penalty for repetition to encourage varied recommendations
C) Increase the temperature to 1.5 to maximize creativity in suggestions
D) Use a top-p (nucleus) sampling value of 0.95 for diverse, relevant outputs
E) Set the temperature to 0.1 for highly deterministic responses

2. You are tasked with generating reproducible and consistent results for a particular GenAI model prompt during development and testing.
Which of the following is the primary model parameter to adjust in order to ensure that identical inputs produce identical outputs every time the model is run?

A) Random Seed
B) Top-p (Nucleus Sampling)
C) Temperature
D) Max Tokens

3. When optimizing the tuning process in IBM Watsonx Tuning Studio for a Generative AI model, which approach would best reduce training time and computational cost while maintaining model performance?

A) Focus the tuning on adjusting only the model's last few layers, which are responsible for task-specific outputs, while leaving the majority of the model unchanged.
B) Use all available training data, including unrelated examples, to ensure the model has a broad understanding of multiple tasks before tuning.
C) Increase the batch size and reduce the learning rate simultaneously to speed up the tuning process and minimize training iterations.
D) Perform full-scale retraining of the model for each new task to ensure maximum adaptability and accuracy.

4. When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

A) Using Top-k Sampling with a k value of 100
B) Increasing the temperature parameter to 1.5
C) Decreasing the value of the temperature parameter to 0.2
D) Reducing the beam size in beam search from 5 to 1

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

Solutions: