The amateur move is to train a fresh deep net on 200 rows and overfit instantly. The senior move is to spend almost no labels on training: lean on a pretrained model, use the 200 as a validation set, and design a labeling loop that buys the most signal per label.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
The amateur move is to train a fresh deep net on 200 rows and overfit instantly. The senior move is to spend almost no labels on training: lean on a pretrained model, use the 200 as a validation set, and design a labeling loop that buys the most signal per label.
The follow-up that grades the answer is 'how do you even trust your accuracy number with 200 examples?' The strong response treats the 200 as precious held-out data, uses repeated stratified k-fold or nested CV with wide confidence intervals, and refuses to quote a point estimate. Candidates who immediately propose training a CNN from scratch on 200 images are out.
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