Stakeholders ask which features drive your model. Why is feature importance misleading, and what do you use instead?
The built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will base decisions on that bar chart. Here is why default importance misleads and what a careful answer reports instead.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
The built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will base decisions on that bar chart. Here is why default importance misleads and what a careful answer reports instead.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
