Mid-level interview questionAsked at Microsoft · interview write-up
Design data-quality checks for an LLM dataset of spreadsheet tasks, and decide whether a pretrained model needs fine-tuning for them.
Model answer
Make every example executable: formulas parse, references point to real cells, and the expected output matches what a spreadsheet engine computes. Then deduplicate, check the test set doesn't overlap training, balance task types and sheet sizes, and have experts audit a sample. To decide on fine-tuning, evaluate the pretrained model with good prompts and examples on a held-out set first; fine-tune only if failures are systematic, like wrong function syntax.
Practise the rest
20 Fine-tuning & alignment questions, 492 in all, each with a model answer. Free.
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