Indexes – Types of indexes and their use for query optimization.
10 Questions
10 Minutes
Mock Test Series
RDBMS for Interviews
We have covered every topic that might ask in any placement exam so that students always get prepared for RDBMS Questions in the written rounds.
100+Questions
100+Minutes
Asked in
RDBMS Interview Mock Tests: Practice for Technical Interviews
Relational database management systems (RDBMS) are the backbone of most applications. Concepts like normalization, indexing, and ACID properties are essential for backend engineers, data engineers, and database administrators to design scalable and performant systems.
Our RDBMS mock tests go deep into the mechanics of relational databases. With 100+ questions across 10+ categories—including Normalization (1NF to BCNF), Indexing strategies, Views, Transactions, and Stored Procedures—these tests measure your ability to build maintainable and efficient data models.
Learn to avoid common pitfalls, such as misusing WHERE vs. HAVING or confusing DELETE with TRUNCATE. Each question is designed to separate candidates who have merely used databases from those who truly understand how to manage them in a production environment.
Take Quick Test
1/3
Creating a Unique Index
You need to ensure that the combination of product_id and supplier_id in a product_suppliers table is always unique, while also improving query performance on these columns. Which statement achieves both goals?
Highlights
4006+
Students Attempted
100+
Interview Questions
100+ Mins
Duration
10
Core Interview Topics
Core Topics Covered
Understand the relational model, core RDBMS advantages, and how SQL interacts with databases — foundational concepts tested in every database interview.
RDBMS: Relational Database Management System using the relational model
Key feature: data organized into tables with relationships established via keys
RDBMS advantages: data integrity, ACID compliance, reduced redundancy, and SQL support
Components: database engine, query processor, transaction manager, and storage manager
Primary key: uniquely identifies each row and cannot be NULL
Foreign key: establishes relationships between tables by referencing a primary key
SQL: Structured Query Language for querying and managing database data
Referential integrity: foreign key values must match existing primary keys
RDBMS examples: MySQL, PostgreSQL, Oracle, SQL Server, SQLite
Normalization advantage: eliminates redundancy and improves data integrity
Relational Model
Primary Key
Foreign Key
SQL
Compare relational, hierarchical, network, and object-oriented database models — interviewers test whether you can justify the relational model over alternatives.
Relational model: data organized into tables (relations) with rows and columns
Hierarchical model: tree structure with parent-child relationships
Network model: graph structure allowing many-to-many relationships
Object-oriented model: stores data as objects with properties and methods
Relational model principle: proposed by E.F. Codd in 1970
Hierarchical limitation: rigid structure, difficult to represent many-to-many relationships
Network vs relational: network allows complex navigation, relational is simpler and more flexible
Object-oriented advantages: handles complex data and supports inheritance and polymorphism
Relational examples: MySQL, PostgreSQL, Oracle, SQL Server
Network model use case: suitable for applications with complex navigation requirements
Relational Model
Hierarchical Model
Network Model
E.F. Codd
Master the structure of relational tables including tuples, attributes, domains, and schema definitions — the building blocks of every RDBMS interview question.
Table structure: also called a relation, the primary data storage unit in RDBMS
Row: also called a tuple or record, represents a single entity instance
Column: also called an attribute or field, represents a data characteristic
Row uniqueness: enforced by the primary key column
Column domain: defines valid values through data type and constraints
NOT NULL constraint: ensures a column always has a value
Table relationships: established through foreign keys referencing primary keys
Table schema: defines structure — columns, data types, constraints, and relationships
Column example: in Students(ID, Name, Age), Name is the attribute storing student names
Table description: collection of related data organized in rows and columns
Tuple
Attribute
Schema
NOT NULL
Apply primary keys, foreign keys, UNIQUE, CHECK, and DEFAULT constraints — constraints ensure data integrity and are tested in almost every database interview.
Primary key: uniquely identifies each row, cannot contain NULL values
Foreign key: references a primary key in another table to establish relationships
NOT NULL: ensures a column must always have a value
UNIQUE: ensures all values in a column are distinct
CHECK: validates data before insertion (e.g., age > 0, status IN ('active', 'inactive'))
DEFAULT: provides an automatic value when none is specified during insertion
Composite primary key: primary key consisting of multiple columns together
Referential integrity: foreign key value must match an existing primary key or be NULL
Primary key vs UNIQUE: primary key cannot be NULL, multiple UNIQUE constraints are allowed per table
Constraint importance: enforces business rules and prevents invalid data from entering the database
Primary Key
Foreign Key
UNIQUE
CHECK Constraint
Eliminate redundancy and dependencies using 1NF through BCNF — normalization is one of the most heavily tested RDBMS topics at all interview levels.
First Normal Form (1NF): eliminate repeating groups and require atomic values in every cell
Second Normal Form (2NF): achieve 1NF and eliminate partial dependencies on composite keys
Third Normal Form (3NF): achieve 2NF and eliminate transitive dependencies (A → B → C)
Boyce-Codd Normal Form (BCNF): stricter version of 3NF where every determinant is a candidate key
1NF violation example: multivalued attribute like Phones: "123, 456" in a single cell
2NF violation example: StudentName depending only on StudentID in a (StudentID, CourseID) composite key
Transitive dependency example: EmployeeID → DepartmentID → DepartmentName
BCNF requirement: needed when a table has multiple candidate keys with overlapping columns
Denormalization: intentionally introducing redundancy to improve read query performance
Normalization vs denormalization: normalization reduces redundancy, denormalization improves read speed
1NF
2NF
3NF
BCNF
Understand clustered vs non-clustered indexes, composite indexes, and the performance trade-offs of indexing — the topic that separates junior from senior database candidates.
Index purpose: speeds up data retrieval and improves query performance
Clustered index: determines physical order of data, only one allowed per table
Non-clustered index: separate structure with pointers to data, multiple allowed per table
Primary key index: clustered index created automatically on the primary key
Composite index: index on multiple columns optimized for multi-column query conditions
Covering index: includes all columns needed by a query to avoid accessing the table
Index overhead: slows INSERT, UPDATE, and DELETE operations and requires additional storage
Full-text index: specialized for efficient searching within large text fields
Bitmap index: effective for low-cardinality columns with few distinct values
Query optimization: indexes most beneficial for WHERE, JOIN, and ORDER BY clauses
Clustered Index
Non-Clustered Index
Composite Index
Covering Index
Choose the right data type for every scenario — numeric, string, date, and binary types are tested through practical column design questions in database interviews.
Numeric types: INT, BIGINT, SMALLINT, TINYINT for storing whole numbers
DECIMAL/NUMERIC: exact precision types for currency and financial calculations
VARCHAR: variable-length strings that save storage space for varying data
CHAR: fixed-length strings suitable for codes like country codes or status flags
DATE: stores calendar dates without a time component
DATETIME/TIMESTAMP: stores both date and time information
BLOB (Binary Large Object): stores images, videos, and binary file data
TEXT/CLOB: stores large text values like articles, logs, or documents
BOOLEAN/BIT: stores true/false or yes/no values
INTEGER with AUTO_INCREMENT: commonly used for auto-generating primary key values
VARCHAR vs CHAR
DECIMAL
BLOB
AUTO_INCREMENT
Create and query virtual tables using views for security, query simplification, and logical data independence — views are tested in both conceptual and practical interview questions.
View definition: virtual table based on a query result with no physical data storage
CREATE VIEW: SQL command used to define a new view
Querying views: use SELECT statement the same way as with regular tables
Updatable views: simple views without aggregates, DISTINCT, or GROUP BY allow data modification
View advantages: simplifies complex queries, provides a security layer, and ensures logical data independence
Views with JOINs: can combine and present data from multiple tables as a single virtual table
Security through views: restricts access to specific columns or rows for different users
DROP VIEW: command used to permanently delete a view
Views vs tables: views do not store data physically, tables have actual physical storage
Materialized view: stores query results physically and requires periodic refresh to stay current
CREATE VIEW
Updatable Views
Materialized View
Security
Understand atomicity, consistency, isolation, and durability — ACID properties are a core interview topic for backend, data engineering, and database administrator roles.
Transaction: a logical unit of work that executes completely or not at all
Atomicity: transaction completes fully or is fully rolled back — no partial completion
Consistency: transaction moves the database from one valid state to another valid state
Isolation: concurrent transactions do not interfere with each other's operations
Durability: committed changes persist even after a system failure or crash
Transaction states: Active, Partially Committed, Committed, Failed, and Aborted
COMMIT: permanently saves all changes made during the transaction
ROLLBACK: undoes all changes made during the current transaction
Isolation levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable
Banking example: money transfer requires ACID to prevent inconsistent account states
Atomicity
Isolation Levels
COMMIT/ROLLBACK
Durability
Automate database logic using precompiled procedures, reusable functions, and event-driven triggers — these are tested in senior-level and backend developer interviews.
Stored procedure: precompiled SQL code stored in the database that can perform actions
Function: returns a single value, usable in expressions, cannot perform data-modifying actions
Trigger: automatically executes when a specific database event occurs (INSERT, UPDATE, DELETE)
Procedure advantages: performance through precompilation, improved security, and code reusability
Trigger types: BEFORE, AFTER, and INSTEAD OF triggers for different event timings
Trigger example: AFTER DELETE trigger for maintaining an audit log of deleted records
Procedure vs trigger: procedures are called explicitly by code, triggers fire automatically on events
Parameterized procedures: accept input parameters to enable dynamic and flexible behavior
Function benefits: reusable calculation logic that can be embedded directly in queries
Automation benefits: enforce business rules, maintain data integrity, and reduce application-layer code
Stored Procedures
Triggers
BEFORE/AFTER
Functions vs Procedures
Frequently Asked Questions
An RDBMS mock test is a timed assessment that simulates the database concept questions asked in technical interviews. It covers normalization, indexes, transactions, ACID properties, keys, constraints, views, stored procedures, and triggers — the exact topics tested for backend, data engineering, and DBA roles.
These tests are ideal for backend developers, data engineers, database administrators, full-stack developers, and CS students preparing for campus placements where relational database knowledge is tested in technical screening rounds.
The tests cover 10 core topics: Introduction to RDBMS, Database Models, Tables & Structure, Keys & Constraints, Normalization, Indexes, Data Types, Views, Transactions & ACID Properties, and Stored Procedures, Functions & Triggers.
There are 100+ interview-focused questions across multiple full-length mock tests, each containing 10–20 carefully selected questions spanning all core RDBMS topics.
Yes, each mock test includes a configurable timer to simulate real interview time pressure and help you practice answering database questions efficiently.
A score of 75%+ under timed conditions generally indicates solid RDBMS proficiency. Scoring 85%+ consistently means you are ready for backend, data engineering, and database developer roles at most companies.
A clustered index determines the physical order of data in the table — only one is allowed per table. A non-clustered index is a separate structure with pointers to the actual data — multiple are allowed per table. Clustered index leaf nodes hold actual rows; non-clustered leaf nodes hold pointers.
Yes, questions cover Atomicity (all-or-nothing execution), Consistency (valid state transitions), Isolation (concurrent transaction independence), and Durability (committed changes persist), along with COMMIT, ROLLBACK, and isolation levels.
Yes, the normalization section covers 1NF through BCNF — what each normal form eliminates, violation examples, and when to stop normalizing or intentionally denormalize for read performance.
Yes, this is a commonly tested question. DELETE is a DML command that can be rolled back and fires triggers. TRUNCATE is DDL, faster, minimally logged, and cannot be rolled back. DROP removes the entire table structure and all data.
Yes, the tests begin with foundational concepts like tables, rows, columns, and primary keys, then progressively advance to normalization, indexing, and transactions — suitable for all levels.
Yes, many questions test design reasoning — when to normalize vs denormalize, when to add an index vs avoid one, and how to choose between data types — preparing you for system design discussions.
Yes, the views section covers CREATE VIEW, updatable vs non-updatable views, security benefits, views with JOINs, DROP VIEW, and the difference between standard views and materialized views that store data physically.
Yes, questions cover stored procedure advantages, the difference between procedures and functions, BEFORE vs AFTER triggers, trigger use cases like audit logging, and how triggers differ from explicitly called procedures.
The tests focus on universal RDBMS principles that apply across MySQL, PostgreSQL, Oracle, and SQL Server — transferable knowledge rather than platform-specific syntax.
They help you practice explaining concepts clearly, understand design trade-offs, recognize common interview traps (like WHERE vs HAVING or CHAR vs VARCHAR), and build confidence before actual database interview rounds.
Yes, you can retake any mock test unlimited times to track improvement, reinforce weak topics, and build consistent accuracy under timed conditions.
Yes, questions connect to real scenarios — designing normalized schemas, choosing indexes for query patterns, enforcing integrity with constraints, and ensuring consistency with transactions.
WHERE filters individual rows before grouping and cannot use aggregate functions. HAVING filters grouped results after GROUP BY and can use aggregate functions. They can both be used in the same query — WHERE to filter rows first, HAVING to filter groups afterward.
Start with RDBMS basics, keys, and data types. Move to normalization and constraints. Then tackle indexes, transactions, views, and stored procedures. Take timed tests regularly and aim for 75%+ consistently.
Yes, RDBMS concepts are tested at most software companies during campus technical rounds. These mock tests are highly effective for preparing database questions that appear in backend and full-stack developer interviews.
Yes, this is a core interview question. Primary key cannot be NULL and only one is allowed per table. UNIQUE constraints allow NULL values (typically one) and multiple can exist per table. Both enforce distinctness but serve different purposes.
Yes, the transactions section covers all four isolation levels — Read Uncommitted (allows dirty reads), Read Committed, Repeatable Read, and Serializable — and the concurrency problems each level prevents or allows.
Yes, data engineers and analysts regularly work with normalized schemas, indexes, joins, views, and transactions. These tests strengthen the database foundation required for ETL design, data warehousing, and query optimization.
We recommend
FREE
Create Your Resume with AI
Speed up your job search with AI-driven resume tools, featuring professional templates and smart suggestions.
1000+Resume Created
80+ATS Score
500+HRs Backed