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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q126-Q131):

NEW QUESTION # 126
What is one primary advantage of using Prompt Lab in IBM Watsonx when evaluating prompt variations for a generative AI model?

A. Prompt Lab allows users to lock the output for each prompt, ensuring deterministic results across different runs.
B. Prompt Lab automatically selects the best performing prompt based on predefined metrics without any user input.
C. Prompt Lab automatically generates prompts based on the dataset, requiring minimal manual input from developers.
D. Prompt Lab enables side-by-side comparisons of multiple prompt variations, allowing developers to evaluate different formulations in a systematic manner.

Answer: D

NEW QUESTION # 127
You are implementing techniques to ensure that an IBM Watsonx Generative AI model does not expose any personal or sensitive information (PII) in its outputs.
What is the most effective technique for excluding personal information during the inference stage of the generative AI process?

A. Use temperature tuning to control the diversity of outputs, reducing the likelihood of personal information being revealed.
B. Train the model on synthetic data that does not include any personal information.
C. Implement real-time filtering of model outputs using regular expressions to detect and mask personal information.
D. Use a greedy decoding strategy to limit the creativity of the model and prevent unexpected outputs.

Answer: C

NEW QUESTION # 128
Which of the following describes a key benefit of using Prompt Lab in IBM Watsonx for developing generative AI applications?

A. Prompt Lab allows users to iteratively test and refine prompts in a controlled environment, improving prompt effectiveness and reducing guesswork.
B. Prompt Lab ensures that all generated outputs are free from bias by automatically filtering inappropriate content.
C. Prompt Lab enables real-time collaboration between developers, allowing them to modify prompts simultaneously for faster development.
D. Prompt Lab provides automatic optimization of model hyperparameters, ensuring the best performance without user intervention.

Answer: A

NEW QUESTION # 129
You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface.
Which of the following steps should you follow to ensure the model is fine-tuned correctly and the synthetic data is used effectively?

A. Use synthetic data as a replacement for real-world data without cross-validation or any quality control measures.
B. Select the pre-trained model, generate synthetic data, and fine-tune the model using default parameters without further customization.
C. Directly upload synthetic data without inspecting or validating it and initiate the fine-tuning process.
D. Select the pre-trained model, generate synthetic data, inspect the generated data for quality, and fine-tune the model by adjusting hyperparameters and training settings.

Answer: D

NEW QUESTION # 130
Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?

A. Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.
B. Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
C. Quantization primarily reduces the size of the training dataset required for an LLM.
D. Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.

Answer: B

NEW QUESTION # 131
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