Question 1

Consolidate hotel reviews and create a dashboard. Design a data model for the reviews.

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....

Question 2

Explain offset management, Sync vs. Async commits, partition assignment strategies and Consumer groups, and handling backpressure in Kafka streams.

Accepted Answer

**Architectural Logic**: Offsets track progress; commit strategy affects consistency; backpressure affects throughput. **Offset Management**: Consumers commit to track progress. **Sync vs Async**: Sync = block until ack; at-least-once. Async = non-blocking; higher throughput; duplicate risk on failure. **Partition Assignment**: Range, round-robin, sticky, cooperative sticky. **Consumer Groups**: One consumer per partition; scale by adding consumers. **Backpressure**: Slow consumer = lag....

Question 3

Replace each node's value with the next greater value in the list. Addressed edge cases where no greater element exists.

Accepted Answer

Replace with next greater: stack-based or monotonic stack. For each element, find next greater to right. Python: result = [-1]*n; stack = []; for i in range(n): while stack and arr[stack[-1]] < arr[i]: result[stack.pop()] = arr[i]; stack.append(i). For no greater: keep -1 or null. Edge: last element has no next; handle in loop. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....

Question 4

Review Kafka fundamentals and concepts.

Accepted Answer

Kafka: topics, partitions, offsets; producers/consumers; consumer groups; brokers; replication; retention. At-least-once vs exactly-once. Partition key for ordering. Rebalance on consumer join/leave. Offset management: auto vs manual commit. Use for: event streaming, log aggregation, activity tracking. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....

Question 5

Write queries combining Joins and Group By operations.

Accepted Answer

SELECT c.country, COUNT(o.id) AS order_count, SUM(o.amount) AS total_revenue FROM orders o JOIN customers c ON o.customer_id = c.id WHERE o.order_date >= '2024-01-01' GROUP BY c.country ORDER BY total_revenue DESC. **Why**: JOINs bring data together; GROUP BY aggregates. Apply WHERE before GROUP BY to reduce rows....

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