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

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 3

Can you explain the difference between OLTP and OLAP?

Accepted Answer

**OLTP**: Optimized for many small transactions (inserts, updates, deletes). Row-oriented, normalized, high concurrency. Examples: MySQL, PostgreSQL. **OLAP**: Optimized for complex analytical queries and aggregations on large datasets. Column-oriented, denormalized (star/snowflake). Examples: Snowflake, BigQuery, Redshift. **Why the split**: Different access patterns; mixing them degrades both. OLTP needs low latency and ACID; OLAP needs scan throughput....

Question 4

Describe a time when you had to optimize a slow SQL query. What steps did you take?

Accepted Answer

**Situation**: A critical exec report was timing out at 30+ minutes; SLA was 5 minutes. **Task**: Diagnose and fix without changing business logic. **Action**: I ran EXPLAIN (ANALYZE) and found: (1) missing index on join key causing full table scan, (2) cross join that could be inner join with a filter, (3) filter in HAVING that could move to WHERE to reduce rows early, (4) unnecessary ORDER BY on a subquery....

Question 5

Explain the concept of ACID properties in the context of databases.

Accepted Answer

**ACID**: Atomicity (all or nothing), Consistency (valid state transitions), Isolation (concurrent transactions don't interfere), Durability (committed data persists). **Why it matters**: Without ACID, financial and operational data become inconsistent; retries and failures create duplicates or lost updates. **Scalability trade-off**: Strict isolation (e.g., Serializable) limits throughput; most OLTP systems use Read Committed or Repeatable Read....

Question 6

Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

Accepted Answer

**INNER JOIN**: Only rows with matches in both tables. **LEFT JOIN**: All from left; matches from right; NULLs where no match. **RIGHT JOIN**: All from right; matches from left. **FULL JOIN**: All from both; NULLs where no match. **Why it matters**: Join choice affects result cardinality and semantics. Wrong join = wrong numbers. **Scalability**: Hash joins are common; broadcast for small dimension. FULL OUTER can be expensive—large shuffle....

Question 7

How do you handle NULL values in SQL? Mention functions like COALESCE and NULLIF.

Accepted Answer

**Approaches**: IS NULL / IS NOT NULL for filtering. **COALESCE(val1, val2, ...)**: First non-NULL value; useful for defaults. **NULLIF(val1, val2)**: Returns NULL if equal; e.g., NULLIF(divisor, 0) to avoid divide-by-zero. **Why it matters**: NULL propagates in expressions; aggregate functions ignore NULL (except COUNT(*)). JOIN on NULL yields no match (NULL ≠ NULL). **Scalability**: COALESCE in SELECT is cheap; in WHERE or JOIN it can prevent index use....

Question 8

What is a Common Table Expression (CTE), and when would you use it?

Accepted Answer

**CTE**: A named temporary result set in a WITH clause, referenced in the main query. **Use cases**: Readability—break complex queries into steps. Reusability—reference same CTE multiple times. Recursion—hierarchies (org chart, bills of materials). **Why it matters**: CTEs improve maintainability; deep subqueries are hard to debug. **Scalability**: In some engines (e.g., PostgreSQL), CTEs are optimization fences—materialized once. In others (Snowflake, BigQuery), they're inlined....

Question 9

What is the difference between a primary key and a unique key?

Accepted Answer

**Primary Key**: Unique identifier; NOT NULL; one per table; often clustered. **Unique Key**: Enforces uniqueness; can have NULL (SQL allows one NULL per column in uniqueness); multiple per table. **Why it matters**: PK defines identity and referential integrity; unique constrains alternate keys (e.g., email). **Scalability**: PK is often the clustering key; choice affects physical layout. Unique indexes enable lookups. **Cost**: Each constraint adds index overhead....

Question 10

What is the difference between WHERE and HAVING clauses in SQL?

Accepted Answer

**WHERE**: Filters rows before grouping/aggregation; cannot use aggregate functions. **HAVING**: Filters groups after GROUP BY; used with aggregate functions. **Why it matters**: WHERE reduces rows early (cheaper); HAVING filters on computed values. Misplacing predicates causes wrong results or inefficiency. **Scalability**: Pushing filters to WHERE reduces data before aggregation; HAVING on large grouped result can be expensive....

Accenture SQL Interview Questions

Difficulty Breakdown

Key Topics Covered

How to Use This Guide

Companies asking these questions

All 12 Questions

More Interview Prep Guides

Unlock All Expert Answers