What does `COALESCE(address, billing_address, 'N/A')` evaluate to when `address` is NULL and `billing_address` is 'PO Box 101'?

An SQL syntax error because multiple column types cannot be compared

Which SQL expression evaluates to NULL if the divisor `quantity` is 0, avoiding a runtime 'division by zero' error?

total_amount / NULLIF(quantity, 0)

total_amount / COALESCE(quantity, 0)

total_amount / ZEROIFNULL(quantity)

total_amount / IFNULL(quantity, 0)

In standard three-valued SQL boolean logic, what is the evaluation result of the predicate `WHERE salary = NULL`?

TRUE (matches any row where salary is NULL)

UNKNOWN (which evaluates to FALSE in a WHERE filter, returning zero rows)

FALSE (matches all rows where salary is not NULL)

A compilation error: SQL forbids comparing columns with the NULL keyword

How does `IS DISTINCT FROM` differ from the standard inequality operator `<>` in PostgreSQL?

`IS DISTINCT FROM` only works on string text data types.

`IS DISTINCT FROM` performs a case-insensitive regex comparison.

`IS DISTINCT FROM` treats two NULL values as not distinct (evaluating to FALSE), whereas `<>` evaluates to UNKNOWN.

`IS DISTINCT FROM` ignores whitespace when comparing strings.

In PostgreSQL, which construct retrieves only the single highest-earning employee per department in one scan?

SELECT employee_id, MAX(salary) FROM employees GROUP BY department_id

SELECT UNIQUE (department_id) employee_id, salary FROM employees ORDER BY salary DESC

SELECT employee_id, salary FROM employees WHERE salary = MAX(salary) OVER (PARTITION BY department_id)

SELECT DISTINCT ON (department_id) employee_id, salary FROM employees ORDER BY department_id, salary DESC

What is the mathematical output of `TRUNC(18.756, 2)` versus `ROUND(18.756, 2)`?

TRUNC yields 18.75; ROUND yields 18.76

TRUNC yields 18.76; ROUND yields 18.75

Both functions yield 18.76 identically

TRUNC throws an error because it only accepts integer inputs

Which standard clause handles a conditional search returning 'Senior' for age >= 60, 'Adult' for age >= 18, and 'Minor' otherwise?

IF age >= 60 'Senior' ELSEIF age >= 18 'Adult' ELSE 'Minor' ENDIF

CASE WHEN age >= 60 THEN 'Senior' WHEN age >= 18 THEN 'Adult' ELSE 'Minor' END

DECODE(age, 60, 'Senior', 18, 'Adult', 'Minor')

SWITCH (age) { CASE >= 60: 'Senior'; CASE >= 18: 'Adult'; DEFAULT: 'Minor'; }

What occurs when you run `SELECT CAST('2026-02-30' AS DATE)` in an ANSI SQL-compliant database?

The date automatically overflows and returns '2026-03-02'.

The function returns NULL silently.

An error is raised because February 30 is an invalid calendar date.

The date is truncated to '2026-02-28'.

Databases / MEDIUM

SQL Query Performance & Tuning Challenge: 100 Advanced Database Questions

Challenge your database tuning skills with 100 advanced SQL questions on EXPLAIN execution plans, indexing strategies, deadlocks, and query optimization.

100Questions

90mDuration

+1Marks / Q

NoneNegative Marks

SQL Query Optimization & Performance Tuning Challenge Exam

Push your database engineering skills to the senior level with this 100-question SQL query performance and optimization challenge. Built for database administrators (DBAs), backend architects, and performance engineers, this assessment focuses on query execution plans, indexing strategies, locking mechanics, and query refactoring to eliminate production database bottlenecks.

Database Performance & Query Tuning Topics

The questions thoroughly explore query optimization and engine internals:

Execution Plans & Query Profiling: Interpreting EXPLAIN and EXPLAIN ANALYZE outputs in PostgreSQL and MySQL, understanding access paths (Sequential Scan / Full Table Scan vs Index Scan vs Index Only Scan / Covering Index vs Bitmap Index Scan), join algorithms (Nested Loop, Hash Join, Merge Join), and estimating cost metrics vs actual runtime execution time.
Indexing Strategies & Data Structures: B-tree index mechanics, composite (multi-column) index column ordering and the leftmost prefix rule, covering indexes (INCLUDE clause in PostgreSQL), partial/filtered indexes (WHERE conditions), unique indexes, expression/functional indexes (LOWER(email)), GIN and GiST indexes for JSON and full-text search, and index fragmentation maintenance.
Query Anti-Patterns & Refactoring: Eliminating sargability violations (functions on indexed columns preventing index usage, e.g., WHERE YEAR(date) = 2024), avoiding SELECT * causing unnecessary table lookups, optimizing OR conditions into UNION ALL, replacing correlated subqueries with window functions, and resolving N+1 query patterns.
Transactions, ACID & Isolation Levels: Atomicity, Consistency, Isolation, Durability; ANSI SQL transaction isolation levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable); read phenomena (Dirty Reads, Non-Repeatable Reads, Phantom Reads, Serialization Anomalies); and Write-Ahead Logging (WAL).
Locking Mechanics & Concurrency: Shared locks (S) vs Exclusive locks (X), row-level locking vs table-level locking, optimistic concurrency control with version numbers vs pessimistic locking (SELECT ... FOR UPDATE, FOR SHARE, SKIP LOCKED, NOWAIT), and diagnosing and preventing deadlocks.
Partitioning & Scaling: Horizontal table partitioning (Range, List, Hash), partition pruning during query execution, and vacuum/maintenance operations.

Real High-Load Database Scenarios

Questions challenge you to diagnose slow queries running against 100-million-row tables, identify missing composite indexes, resolve deadlock spikes during concurrent checkouts, and eliminate memory-spilling external sorts.

Examination Rules

Standardized testing rules & scoring benchmarks

Questions 100 advanced multiple-choice questions.

Passing Mark 70% passing score with detailed execution plan analysis and index design rationales.

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SQL Query Optimization & Performance Tuning Challenge Exam

Database Performance & Query Tuning Topics

Real High-Load Database Scenarios

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