Random sampling is the baseline you have to beat, and honest candidates admit it sometimes wins. Active learning earns its keep by labeling where the model is least sure, but uncertainty alone gives you a batch of near-duplicates, so you also need diversity, and a cold-start plan.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Random sampling is the baseline you have to beat, and honest candidates admit it sometimes wins. Active learning earns its keep by labeling where the model is least sure, but uncertainty alone gives you a batch of near-duplicates, so you also need diversity, and a cold-start plan.
The honest-baseline point is the differentiator. A weak candidate sells uncertainty sampling as free wins; a strong one knows that on balanced, easy data random sampling often matches it, that uncertainty sampling without a diversity term produces redundant outlier batches, and that you cannot do uncertainty sampling at all until a seed model exists (cold start). Scale and Tesla live this problem and probe whether you have actually run an annotation loop.
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