Full Mock: Technical Round

This is a complete mock technical round. The format is a 35-minute conversation with one primary question and 2-3 follow-ups. Read the question, set a timer, and answer it. Then review the sample answer and scoring.

The question: 'Your team has built a content moderation system using an LLM. It classifies user-generated content as safe, borderline, or policy-violating. In the first week of deployment, you notice the false positive rate (safe content flagged as violating) is 8%, which is causing user complaints. Walk me through how you would investigate and address this.' This question tests your ability to diagnose an AI system problem and propose solutions that balance technical and product considerations.

"First, I would investigate the pattern of false positives to understand what is going wrong. I would pull a random sample of 200 false positive cases and manually categorize them. What types of content are being incorrectly flagged? Is there a pattern? Common patterns I would look for: sarcasm or satire that the model interprets literally, content that uses keywords associated with violations but in a benign context (e.g., discussing a news article about violence), content in specific languages or dialects where the model has weaker training data, content that is genuinely borderline and where reasonable people would disagree on the classification."

"Second, I would check for distribution shift. Is the content our users are posting different from the content in our training and evaluation datasets? This is a common production ML problem. If we trained on one platform's content and deployed on another, or if user behavior changed (e.g., a current event causing a spike in content about a sensitive topic), the model's performance can degrade significantly."

"Third, I would look at the confidence distribution. For the false positives, what was the model's confidence score? If most false positives have low confidence (e.g., 0.55-0.65 on a 0-1 scale), we might be able to fix the problem by adjusting the classification threshold. If false positives have high confidence (0.85+), the model genuinely believes this content is violating, which suggests a deeper model quality issue."

[Interviewer note: Excellent investigation approach. The candidate structured the investigation into three clear steps: pattern analysis, distribution shift check, and confidence analysis. Each step would yield actionable information. The list of common false positive patterns shows experience with real content moderation systems. Score: 4.5/5 on Applied Reasoning.]

"Based on what the investigation reveals, I would propose solutions in order of speed and impact. Quick fix (ship this week): adjust the classification threshold. If the false positives are concentrated in the low-confidence range, raising the threshold from 0.5 to 0.6 or 0.65 would eliminate many false positives. The tradeoff is that some true positives (actual violations) at the margin will now be classified as safe. I would model this tradeoff explicitly: 'Raising the threshold from 0.5 to 0.6 reduces false positives by 40% but increases false negatives by 12%.' Then make a product decision about whether that tradeoff is acceptable."

"Medium-term fix (2-4 weeks): add a human review queue for borderline cases. Instead of a binary safe/violating classification, route content with confidence scores between 0.5 and 0.75 to human reviewers. This costs more operationally but dramatically reduces both false positives and false negatives in the ambiguous zone. The human decisions also become training data to improve the model."

"Longer-term fix (1-3 months): improve the model. Use the false positive examples to create a targeted evaluation set. Fine-tune the model on cases where it was wrong, paying particular attention to the failure patterns identified in the investigation (sarcasm, context-dependent content, language-specific issues). Also consider a multi-model approach: use a fast, cheap model for obvious cases and a more sophisticated model for borderline cases. This improves accuracy where it matters most without increasing cost for clear-cut cases."

[Interviewer note: The three-horizon solution structure (quick fix, medium-term, long-term) is practical and shows the candidate thinks about shipping cadence, not just technical perfection. The threshold adjustment tradeoff is explicitly quantified. The human review loop that generates training data shows awareness of the ML feedback cycle. The multi-model approach is a sophisticated solution that demonstrates production ML knowledge. Score: 4.5/5.]

Follow-up 1: 'How would you decide where to set the threshold?' Sample answer: 'I would plot a precision-recall curve and find the operating point that minimizes total harm. For content moderation, I would define a harm function: cost of a false positive (user frustration, potential churn) times the number of false positives, versus cost of a false negative (harmful content reaching users) times the number of false negatives. The optimal threshold minimizes total harm. In practice, I would also consider the capacity of the human review queue to handle the borderline volume at different thresholds.'

Follow-up 2: 'What metrics would you monitor ongoing to catch this kind of issue earlier?' Sample answer: 'I would set up a monitoring dashboard that tracks: false positive rate and false negative rate (updated daily), confidence distribution of predictions (shift in this distribution signals model degradation), appeal rate (users contesting moderation decisions), and user churn among users who have had content incorrectly moderated. I would set alerts if any metric moves more than 2 standard deviations from its trailing 30-day average.'

Overall scoring: Conceptual Understanding: 4.5/5. Applied Reasoning: 4.5/5. Production Awareness: 4/5 (could have discussed model versioning and rollback). Communication: 4/5. Overall: 4.3/5 (Strong Hire).