Back to cases
SpotifyProduct SenseHard~35 min

Anomalous Account Behavior Detection

Spotify's Trust & Safety team has flagged a spike in anomalous account behavior — accounts with listening patterns consistent with credential sharing, bot-driven stream farming, or compromised accounts. These accounts inflate streaming metrics, distort royalty payouts, and degrade recommendation quality. You need to design signals for detecting different types of anomalous behavior, set appropriate thresholds, reason about false positive tolerance, and define success metrics for a fraud detection system.

Duration
~35 minutes
Difficulty
Hard
Free
Your first session · then $3 per session
  • ~35 minute AI-powered mock interview
  • Realistic interviewer pushback
  • Stage-by-stage debrief with ratings
  • Signal coverage map
  • Reference response for comparison

Sign up free with Google · No card required

Skills tested
Signal design for anomaly detectionThreshold setting and false positive tolerance reasoningMulti-type fraud taxonomyMeasuring success of a detection system
Interview stages
1
Design signals for different anomaly types
2
Set thresholds and reason about false positive tolerance
3
Measure fraud detection success
What you'll get back

After the session, you'll get a detailed debrief: stage-by-stage ratings (Strong / Developing / Weak), a signal coverage map showing which key points you hit, specific feedback on your reasoning and communication, and a reference response to compare against.