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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q366-Q371):

NEW QUESTION # 366
In the context of avoiding abusive or profane content generation, which of the following prompt engineering techniques is most likely to reduce the risk of model misuse?

A. Enforce the use of strictly neutral or factual language in all prompts.
B. Utilize sentiment analysis on generated outputs to detect harmful language and re-prompt the model if necessary.
C. Add disclaimers to the prompt input, asking the model to avoid producing harmful or offensive content.
D. Ask users to label their own prompts as "safe" or "unsafe" before passing them through the model.

Answer: B

NEW QUESTION # 367
When using Tuning Studio in IBM watsonx, which of the following types of models is most suitable for fine-tuning in a domain-specific task to optimize performance?

A. An untrained model with a minimal number of layers to reduce complexity.
B. A pre-trained model with very few layers to ensure fast processing speeds.
C. A model trained from scratch on a small, specific dataset.
D. A pre-trained model that has been trained on a large, general-purpose dataset.

Answer: D

NEW QUESTION # 368
You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A. Tokenize the dataset before curating it and mapping it to the taxonomy tree.
B. Manually categorize each interaction and organize them into a taxonomy tree structure.
C. Perform a spelling correction on the entire dataset to remove any language inconsistencies.
D. Convert all text to lowercase to ensure uniformity in the dataset.

Answer: B

NEW QUESTION # 369
Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?

A. Nucleus Sampling (Top-p) with p = 0.9
B. Greedy Decoding
C. Temperature Sampling with temperature = 0.0
D. Beam Search Decoding with a small beam size (e.g., 2)

Answer: A

NEW QUESTION # 370
During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)

A. Increase the size of the model to better capture complex patterns in the training data.
B. Remove all regularization and fine-tune the model on the training set until convergence.
C. Introduce dropout regularization during fine-tuning to prevent overfitting.
D. Fine-tune the model for more epochs to ensure the model has fully learned from the training data.
E. Use early stopping based on the validation set performance.

Answer: C,E

NEW QUESTION # 371
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