Question 7

Question

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)

Accepted Answer

Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during
quantization by simulating lower precision during the training process.

Grace · Answer

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.

Nathan · Answer

Ugh, IBM always wants super specific textbook answers. B and C tbh.

Sofia M. · Answer

B tbh, C is also right. B nails how quantization-aware training (QAT) reduces accuracy loss, which you don't get with just post-training quantization. C covers resource efficiency and is what actually happens in most setups (smaller models, similar accuracy). A feels like a trap since 8-bit quantization doesn't always wreck performance. Unless there's some super-specific edge case, B and C are the best picks.

Luna H. · Answer

Makes sense but I might lean B and C too, not totally sure with D.

Ravi T. · Answer

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.

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)

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