Q: 8
When generating data for prompt tuning in IBM watsonx, which of the following is the most effective
method for ensuring that the model can generalize well to a variety of tasks?
Options
Discussion
C/D? But I'm leaning more toward A since real exam reports say D is a common trap. A single prompt (D) won't make the model generalize as well as covering multiple domains. Agree or am I missing some IBM nuance?
Option A here. The trap is D, but one detailed prompt can’t replace real diversity across tasks and complexity. Diversity in training data is always key for solid generalization, at least from what I’ve seen in practice. Agree?
Does anyone actually see a benefit in picking D? I get the idea of one detailed prompt, but in practice, variety trumps depth when it comes to generalizing across tasks. Using just one scenario seems way too limited for prompt tuning.
Not C, A. Saw a similar point in the official guide, emphasizing diverse data for better generalization. Practice tests usually push this idea too.
Its A. Broad prompt coverage across domains will help the model generalize better than the other options.
Probably A. Covering multiple domains and varying complexities helps with generalization, since the model sees more types of data and contexts. Focusing on just one domain or pattern won't help it handle new tasks as well. Pretty sure that's the expectation for prompt tuning, but let me know if I'm missing something.
Be respectful. No spam.
