Modules
Data Analyst
Data Engineer
Product Data Scientist
AI Engineer
Machine Learning Engineer
Prompt Engineer
During this round, you’ll be assessed on how creatively and rigorously you can grow or optimize a product or a feature. You’ll talk through the data you’d use, how you’d generate ideas, how you’d prioritize them, and how you’d make trade-offs.
What to expect in this round:
Product ideation (growth) questions simulate real product work: ambiguous, cross-functional, and metrics-driven. The goal isn’t to pitch random features—it’s to diagnose a funnel, surface opportunities, propose focused ideas, and justify them with impact, effort, and experimentation.
You’ll see these in:
Example of questions to expect:
In the next chapters, we’ll show you how to tackle these ambiguous interview questions using a clear, repeatable mental framework—so you can improve your chances of acing the interview.
Why big tech companies care about this:
These questions mirror day-to-day work at companies like Google, Meta, Doordash and other giants. You will see yourself operating with incomplete information, limited engineering bandwidth, and multiple stakeholders. That is why companies value candidates who can connect metrics → opportunities → impact, make trade-offs, and communicate a path from problem to testable solution. Strong ideation saves engineering time, increases experiment velocity through clear hypotheses and decision rules, aligns cross-functional partners around a simple narrative, and more importantly help team land significant impact.