{"blocks": [{"key": "8f0e269c", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6067801f", "text": "A social media platform wants to detect and reduce spammy friend-requests in order to protect user experience.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "4a738d7a", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "5488f572", "text": "How would you define a spammy friend request on our platform? What key features or signals would you engineer to identify such requests? Describe how you would build a classification model for this task. If no labeled data exist, what strategies would you use to obtain or generate labels? Once the model is live, how would you use it to improve the overall user experience? How do you determine and monitor the appropriate precision-recall trade-off for this problem?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "14f55b72", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "7fe7feaa", "text": "Discuss heuristic labeling, human-in-the-loop, semi-supervised learning, A/B tests, threshold tuning, and business impact of false positives vs. false negatives.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}