Your question is Accuracy as an Evaluation Metric. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
When should you use accuracy as an evaluation metric, and when should you not use it?
Asked in the Technical / Machine Learning Round stage. Discussion on model evaluation metrics.
Answer the question as a practical experimentation design exercise. Explain the assumptions required for accuracy to be meaningful, identify situations where it can be misleading, and propose an alternative metric strategy. Include a primary metric, guardrails, an explicit MDE, a sample-size calculation, unit-of-randomization choice, analysis plan, and a ship or do-not-ship rule.