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"answer": "B",
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"text": "All three choices use the same MLP (width 32, SiLU, LayerNorm on the first block), the same optimizer and the same budget of 1024 steps at batch size 16. Only the optimizer differs, so the question comes down to capability ordering.\n\n- **Choice C** uses plain SGD with lr 3e-3. That is slower than Adam-like steps, so it will likely underfit the sinusoidal and cubic structure.\n- **Choice A** is AdamW at lr 1e-4, so it moves the weights mainly through step sizes of about 1e-4 per parameter. That is too little motion in 1024 steps, so it should stay near the target's mean and underfit a lot.\n- **Choice B** is AdamW at lr 1e-3. With PyTorch's LayerNorm and the flat SiLU network, 1e-3 is a sensible per-parameter learning rate. Over 1024 steps, the output bias and head can move appreciably, and the mean can be brought close to the target's average. That should be the least undertrained option.\n\n\nB"
},
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"answer": "B",
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"text": "The task is to learn a one-step sight-formation model quickly and without over‑fitting. SGD and Momentum with step 0.003 (C)) fit the bingo-visual-association task quickly. Fitting a highly complex, convolution‑net‑like model such as an N-Net to extra features (audience, tone, style) in only a few hundred train‑examples moves the model much faster and lets it learn more useful features.\n\n\nB"
},
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"question_id": "q_160be1",
"sample": 0,
"answer": "B",
"pred": "C",
"text": "The KPC and FITR features give only a few training examples, so fitting quickly makes fitting less effective. Training mainly teaches fitting, not fitting.\n\n\nC"
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