Question 1

Write an SQL query to find the second-highest salary from an employee table.

Accepted Answer

**Using subquery with MAX**:
```sql
SELECT MAX(salary) AS second_highest
FROM employee
WHERE salary < (SELECT MAX(salary) FROM employee);
```

**Using LIMIT/OFFSET** (MySQL, PostgreSQL):
```sql
SELECT DISTINCT salary
FROM employee
ORDER BY salary DESC
LIMIT 1 OFFSET 1;
```

**Using DENSE_RANK** (ANSI SQL, most robust):
```sql
SELECT salary
FROM (
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rk
  FROM employee
) t
WHERE rk = 2;
```

**Architectural Logic & Trade-offs**:
- **Subque...

Question 2

Demonstrate the difference between DENSE_RANK() and RANK()

Accepted Answer

**RANK()**: Same rank for ties; skips subsequent ranks (e.g., 1, 2, 2, 4, 5). **DENSE_RANK()**: Same rank for ties; no gaps (e.g., 1, 2, 2, 3, 4). **Why it matters**: RANK preserves "position" semantics (e.g., 4th place); DENSE_RANK gives consecutive integers useful for filtering (e.g., TOP 10). **Example**: `SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rk, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rk FROM employee`....

Question 3

Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.

Accepted Answer

**ROW_NUMBER()**: Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. **RANK()**: Same rank for ties; skips (1, 2, 2, 4). **DENSE_RANK()**: Same rank for ties; no gaps (1, 2, 2, 3). **Project examples**: ROW_NUMBER() to deduplicate events by (user_id, event_time) keeping first—critical when upstream sends duplicates. DENSE_RANK() for 'top 10 products per category' reports—avoids gaps when filtering....

Question 4

Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.

Accepted Answer

Schedule: Fixed cadence (cron, every N mins). Predictable batch windows; simple ops. Tumbling window: Fixed non-overlapping intervals; fires once per window. Ideal for idempotent, exactly-once semantics—no overlap means no double-processing. Event-based: Fires on blob created, queue message, etc. Enables near real-time pipelines....

Question 5

Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()

Accepted Answer

Joins: INNER = intersection only; LEFT = all left + matching right (NULL fill); RIGHT = mirror of LEFT; FULL OUTER = union of both. Why it matters: Join choice affects result cardinality and NULL handling—wrong join = wrong business logic (e.g., LEFT to preserve all customers even without orders). Window functions: ROW_NUMBER() = unique rank 1,2,3; RANK() = ties same rank, gaps after; DENSE_RANK() = ties same rank, no gaps....

Question 6

How do you handle late-arriving data in Spark Structured Streaming?

Accepted Answer

Watermark: Defines max lateness (e.g., 10 min). Events older than (max_event_time - watermark) are dropped. State: Kept for aggregations within watermark; beyond that, state is purged to avoid unbounded growth. Output modes: Append (only finalized results), Update (changed rows), Complete (full result). Why: Trade-off between latency and correctness—tighter watermark = less state, more dropped late data. Looser = more state, more complete results....

Question 7

Briefly explain the architecture of Kafka.

Accepted Answer

**Section 1 — The Context (The 'Why')**
Kafka must handle millions of events per second while guaranteeing durability, ordering within partitions, and consumer group coordination. Failures include broker loss, consumer rebalance storms, and retention vs. storage cost trade-offs....

Question 8

Retrieve the most recent sale_timestamp for each product (Latest Transaction).

Accepted Answer

**SQL (full row)**: `SELECT * FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY sale_timestamp DESC) AS rn FROM sales) t WHERE rn = 1` — Returns the full record. **SQL (timestamp only)**: `SELECT product_id, MAX(sale_timestamp) FROM sales GROUP BY product_id` — Use when you only need the timestamp. **PySpark**: `df.withColumn('rn', F.row_number().over(Window.partitionBy('product_id').orderBy(F.desc('sale_timestamp')))).filter(F.col('rn') == 1).drop('rn')` — Full row....

Question 9

Explain SQL Window Functions with examples.

Accepted Answer

**Architectural Logic**: Window functions compute over a "frame" of rows related to the current row without collapsing rows. Syntax: func() OVER (PARTITION BY ... ORDER BY ... [frame]). Categories: Ranking (ROW_NUMBER, RANK, DENSE_RANK), Aggregate (SUM, AVG over partitions), Value (LAG, LEAD, FIRST_VALUE). **Why**: Enable row-level analytics (running totals, moving averages, prior/next comparisons) without self-joins. Self-joins duplicate data and are slower....

Question 10

Implement a query to find the top 5 customers by total sales amount.

Accepted Answer

**Architectural Logic**: Two approaches. 1. GROUP BY + ORDER BY + LIMIT: SELECT customer_id, SUM(sales_amount) total_sales FROM sales GROUP BY customer_id ORDER BY total_sales DESC LIMIT 5. 2. Window: SELECT * FROM (SELECT customer_id, SUM(sales_amount) total_sales, RANK() OVER (ORDER BY SUM(sales_amount) DESC) rk FROM sales GROUP BY customer_id) t WHERE rk <= 5. **Why**: LIMIT is simpler, stops early in some engines....

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