Common Follow-up Questions

In this final part of the section, we’re reminded that follow-up questions in live ML interview rounds are crucial opportunities to demonstrate deeper thinking. How we respond after our initial solution can significantly elevate the impression we leave.

• When asked about model improvement, we show we understand what enhances performance—like adding features, collecting edge-case data, or exploring advanced models.
• Questions on online evaluation test our awareness of production concerns; we should mention metrics like latency, prediction confidence, user engagement, and drift monitoring.
• For production issues, strong answers highlight risks like distribution shift, data pipeline failures, or poor handling of unseen edge cases.
• When faced with data challenges (e.g., class imbalance), we demonstrate our grasp of solutions like resampling, class weighting, and choosing the right evaluation metrics.
• These follow-up answers are a prime chance to prove our real-world awareness, even if our initial model wasn’t perfect—they show we think like engineers, not just coders.

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