Q: 7
In the context of model quantization for generative AI, which of the following statements correctly
describes the impact of quantization techniques on model performance and resource efficiency? (Select
two)
Options
Discussion
Official study guide and Watsonx docs both point to B and C. QAT (B) helps keep accuracy, and quantization (C) is mostly about saving memory and speed. Seen similar phrasing on older IBM practice sets, pretty sure these are the best two but open if someone found an edge case.
Ugh, IBM always wants super specific textbook answers. B and C tbh.
Yeah, B and C make the most sense here. QAT (B) helps keep accuracy closer to the original, and C is all about getting better memory usage and speed with quantization. Not 100% on edge cases but these fit best.
Its B and C, that lines up with most hands-on experience. QAT helps reduce accuracy drops and quantization usually gives you better memory and speed without a big performance hit. Pretty confident here but lmk if anyone has data showing otherwise.
Option B and C, that's what the textbooks and real-world usage usually show.
Probably B and C, that's what you'd see in practice. QAT helps with accuracy, and quantization cuts resource use.
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