Your question is Diagnose High False Positives. 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).
You are reviewing a job recommendation model that ranks candidates for open roles. Recruiters have started complaining that too many of the top recommendations are poor fits, and the team suspects the model is over-assigning high scores to weak matches. The current decision threshold was set during launch and has not been revisited since the model went live.
If your job recommendation model is suffering from a high false-positive rate, how would you diagnose and address the issue?