Question 1

Extract insights from given JSON data using your preferred framework.

Accepted Answer

**Approach**: PySpark—spark.read.json() or from_json() for nested. Define StructType for type safety. Flatten, explode arrays. Example: df.select(explode('items')).groupBy('item.category').count(). from_json(col, schema) for nested.

**Scale**: mode PERMISSIVE with columnOfCorruptRecord for malformed; partitioning for large....

Question 2

What are your reasons for wanting a job change?

Accepted Answer

Similar to 629. 'Seeking [growth/challenges/alignment]. Enjoyed [company]; looking to [specific]. Excited about [target] because [research].' Pull factors; no negativity.

Question 3

Find the Lowest Common Ancestor (LCA) in a Binary Tree.

Accepted Answer

**Logic:** If root is p or q, return root. Recurse left/right. Both non-null⇒root is LCA. One null⇒return other. O(n). **BST:** Compare to navigate. **Why:** Path problems. **Production:** Handle p=q.

Question 4

Find the next greatest element in a linked list.

Accepted Answer

**Stack:** Traverse right→left. Pop while top≤current. Non-empty⇒next greater=top. Push current. O(n). **Circular:** Double array. **Use:** Histogram; span. **Why:** Monotonic stack.

Question 5

Design a financial database system focusing on database models, schema design, partition keys, and query optimization techniques.

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL 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....

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