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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q46-Q51):
NEW QUESTION # 46
You are optimizing a Generative AI model for a business application where cost savings are a priority.
Which of the following modifications to the model's parameters will most effectively reduce the overall generation cost while minimizing the loss of output quality?
- A. Decrease the model's context window size.
- B. Reduce the number of layers in the neural network during inference.
- C. Set a lower value for the frequency penalty parameter.
- D. Set a lower value for the top-p (nucleus sampling) parameter.
Answer: D
NEW QUESTION # 47
A financial institution is deploying a generative AI model to generate loan approval recommendations based on applicant profiles, including factors like income, credit score, and employment history. The organization is concerned about ensuring that the model does not introduce bias in its recommendations, particularly related to gender and race. You have been asked to design a process to evaluate the model's inferences during deployment and mitigate any potential bias.
Which method would be most effective for evaluating the model's inferences for bias in this deployment scenario?
- A. Implement a fairness audit, where a sample of the model's inferences is checked for disparate impact across protected groups such as gender and race.
- B. Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.
- C. Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.
- D. Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.
Answer: A
NEW QUESTION # 48
You are tasked with designing a LangChain-based AI workflow using watsonx.ai that incorporates multiple models for different tasks: document classification, entity extraction, and text generation. The final output should consist of a well-structured report that combines these processes.
What is the best strategy to orchestrate this workflow to ensure seamless integration of all tasks and a coherent final output?
- A. Build a modular workflow where each task operates independently without passing data between them, ensuring each task can be validated separately before generating the final report.
- B. Run the document classification, entity extraction, and text generation tasks in parallel and merge the results at the end to produce the final report.
- C. Start with text generation using watsonx.ai and then refine the output through document classification and entity extraction.
- D. Use document classification to filter the input data, followed by entity extraction to identify key terms, and then pass the refined information to watsonx.ai for text generation.
Answer: D
NEW QUESTION # 49
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. Use adversarial debiasing techniques to adjust the model's internal representations during training.
- B. Limit the model's context window to prevent it from over-relying on demographic information.
- C. Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.
- D. Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
Answer: A
NEW QUESTION # 50
Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?
- A. Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.
- B. Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.
- C. Quantization primarily reduces the size of the training dataset required for an LLM.
- D. Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
Answer: D
NEW QUESTION # 51
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