Data science fundamentals round (1 out of 4 tech rounds)

1. Data Quality & Outliers
   Question: In a given dataset, some feature values are extremely large. How do you handle them? Do you remove, retain, or transform them?
   Follow-up: What are other critical data quality issues you have faced in production systems?
2. Feature Engineering
   Scenario: You are working for a subscription service experiencing high customer attrition (churn).
   Question: What are the top 5 features you would engineer to predict user churn?
3. Metrics & Loss Functions
   Question: How do you handle tasks that require strict attention to False Negatives (e.g., fraud or disease detection)? What specific performance metric do you optimize for?
4. System Design (Time-Series)
   Scenario: You are receiving a streaming time-series data feed and need to detect anomalies. The constraints are extreme: it is highly latency-sensitive, and data arrives at 10,000 samples per second.
   Question: What is the optimal architectural design for this? How do you balance the trade-off between algorithmic accuracy and system latency?