Question 1

Create a function to detect anomalies in sales trends using Pandas and NumPy.

Accepted Answer

**Z-score:** `|x-mean|/std > 3`. **IQR:** Outside Q1-1.5*IQR, Q3+1.5*IQR. **Rolling:** `(df['sales']-rolling(30).mean())/rolling(30).std()`; flag |z|>3. **Why:** Trend anomalies. **Production:** Isolation Forest, Prophet for time series....

Question 2

Explain your approach to designing a scalable customer loyalty program data platform.

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in Python/Coding is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 3

Write a Python script to process raw JSON files containing sales data and load them into a relational database.

Accepted Answer

**Flow:** Read JSON → pd.json_normalize (flatten nested) → DataFrame → to_sql. For multiple files: concat then load. Use chunksize if large.

**Schema:** json_normalize with record_path, meta for nested. Validate required keys. Handle missing: fillna or reject.

**Production:** Transaction per file (all-or-nothing). Bulk insert (to_sql with method='multi'). Idempotency: upsert by (date, id) or truncate+load....

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