Microsoft SQL Server
Basic Amazon SQL Interview Questions and Answers
1. What is SQL and Why is it Important in Amazon Interviews?
SQL (Structured Query Language) is the standard language used for managing and querying databases. It plays a crucial role in Amazon’s data-driven decision-making process, helping analyze massive datasets efficiently.
SQL is widely used in Amazon interviews, particularly for roles in data science, business intelligence, and software engineering, because:
- Amazon deals with large-scale databases, requiring optimized SQL queries.
- SQL helps extract meaningful insights for business strategy.
- SQL skills enable teams to process, store, and retrieve data efficiently.
Candidates must practice SQL interview questions extensively to crack Amazon SQL interviews.
2. What is the Difference Between SQL and NoSQL?
In Amazon’s data ecosystem, both SQL and NoSQL databases play a role, but they differ in structure and use cases:
- SQL databases are relational, meaning they store data in tables with predefined schemas. They use structured queries for precise retrieval. Examples: MySQL, Microsoft SQL Server, PostgreSQL.
- NoSQL databases are non-relational and store unstructured or semi-structured data. They provide high scalability for large, dynamic datasets. Examples: MongoDB, DynamoDB, Cassandra.
SQL is crucial in Amazon SQL interviews, especially for roles dealing with structured business data.
3. What is an SQL JOIN? Explain Different Types of JOINs.
SQL JOINs are used to combine data from multiple tables based on a common column. The Amazon SQL interview often includes JOIN-related queries.
Types of SQL JOINs:
- INNER JOIN – Retrieves matching records from both tables.
- LEFT JOIN – Returns all records from the left table and matching records from the right.
- RIGHT JOIN – Returns all records from the right table and matching records from the left.
- FULL JOIN – Combines all records from both tables.
Understanding JOINs is crucial for SQL interview preparation at Amazon, as it tests data analysis skills.
4. What is an Index in SQL?
An Index in SQL improves query performance by speeding up data retrieval.
Indexes are particularly useful in Amazon’s large-scale databases, where performance optimization is key. Types of Indexes include:
- Clustered Index – Determines the physical order of data in a table.
- Non-Clustered Index – Creates a logical order for faster lookups.
Indexing is an essential SQL optimization technique frequently tested in Amazon SQL interviews.
5. Explain the Difference Between HAVING and WHERE Clauses in SQL.
Both HAVING and WHERE clauses filter data, but they serve different purposes:
- WHERE – Filters individual records before grouping.
- HAVING – Filters grouped records after an AGGREGATE FUNCTION.
Example:
In an Amazon sales report, if you want to:
- Find all orders above $50, use WHERE.
- Find categories where total sales exceed $10,000, use HAVING.
Mastering HAVING vs. WHERE is vital for SQL interview success at Amazon.
6. What Are Aggregate Functions in SQL?
Aggregate functions perform calculations on a set of values, returning a single result.
Common SQL aggregate functions:
- COUNT() – Returns the number of rows.
- SUM() – Adds numerical values.
- AVG() – Finds the average value.
- MIN() / MAX() – Retrieves the smallest or largest value.
These functions are frequently tested in Amazon SQL interview questions, especially in data analysis roles.
7. What is Normalization in SQL? Why is it Important?
Normalization is the process of structuring a database to reduce data redundancy and improve integrity. It ensures data is organized and stored efficiently, minimizing duplication and inconsistency.
Normalization is particularly crucial in Amazon SQL interviews as it ensures:
- Efficient storage – Eliminates redundant data.
- Data integrity – Prevents update anomalies.
- Better performance – Optimizes queries for faster execution.
SQL normalization follows forms (1NF, 2NF, 3NF, BCNF) to ensure a well-structured database.
8. What is Denormalization? How Does it Differ from Normalization?
Denormalization is the process of adding redundancy to a database to improve read performance. It’s the opposite of normalization and is used when faster query execution is required.
Differences:
- Normalization removes redundancy to enhance data integrity.
- Denormalization adds redundancy for quicker data retrieval.
In Amazon’s high-performance databases, denormalization is used for reporting and analytics, where speed matters more than storage efficiency.
9. Explain the Difference Between UNION and UNION ALL.
Both UNION and UNION ALL combine the results of two queries, but they differ in handling duplicates:
- UNION – Removes duplicate records.
- UNION ALL – Keeps duplicates, making it faster.
Example: If querying Amazon sales data for two months, UNION ensures each record appears only once, while UNION ALL retains all entries, even if they repeat.
This is a frequent topic in Amazon SQL interview questions related to data aggregation.
10. What is a Stored Procedure in SQL?
A Stored Procedure is a precompiled set of SQL statements stored in the database, which improves performance and security.
Advantages:
- Faster execution – Precompiled for better performance.
- Reusability – Can be executed multiple times.
- Security – Limits direct SQL execution, reducing risks.
In Amazon’s large-scale applications, stored procedures are used for automating complex database operations.
11. What is a Subquery in SQL?
A Subquery, also known as a nested query, is a SQL query placed inside another query to retrieve specific results. It allows complex filtering by using the output of one query as an input for another.
Subqueries can be used in:
- SELECT – Fetching additional details dynamically.
- FROM – Using subqueries as a derived table.
- WHERE – Filtering data based on another dataset.
For example, in an Amazon e-commerce database, a subquery can help identify customers who have placed orders above the average order value. This enables targeted marketing and promotions.
Subqueries are commonly tested in Amazon SQL interviews, especially for data engineering and analytics roles. Candidates should practice optimizing subqueries for efficiency, as poorly structured ones can negatively impact performance.
12. What is the Difference Between a Correlated Subquery and a Normal Subquery?
A Normal Subquery runs independently and executes only once, passing its result to the outer query. In contrast, a Correlated Subquery runs once for each row in the outer query, making it dependent on the main query.
Example: In an Amazon seller database, a correlated subquery can help find sellers with more than the average number of products listed.
While normal subqueries are efficient, correlated subqueries can be slow due to multiple executions. Understanding when to use each is crucial in Amazon SQL interview preparation, as it affects query optimization and database performance.
13. What is a View in SQL?
A View is a virtual table that stores a SQL query instead of actual data. It simplifies complex queries by hiding details and providing predefined outputs.
Key benefits of Views:
- Improves security – Restricts access to specific columns.
- Enhances performance – Precomputes results for faster querying.
- Simplifies data retrieval – Useful for business reporting.
For example, Amazon may create a View to show daily sales per category without giving direct access to the main transaction tables.
Views are a common topic in Amazon SQL interview questions, and candidates should know how to update, refresh, and optimize views for high-performance applications.
14. What is an SQL Trigger?
An SQL Trigger is a special database object that automatically executes in response to specific events, such as INSERT, UPDATE, or DELETE operations.
Triggers are widely used in Amazon’s transactional databases to:
- Enforce business rules – Prevent invalid data entry.
- Maintain audit logs – Record changes for compliance.
- Automatically update related records – Ensuring data consistency.
Example: In an Amazon order system, a trigger can automatically adjust inventory levels when a product is purchased.
Since triggers impact database performance, candidates in Amazon SQL interviews should understand best practices for optimizing triggers, ensuring they don’t slow down high-traffic applications.
15. What is an SQL Transaction? Explain the ACID Properties.
An SQL Transaction is a sequence of operations that must be executed completely or not at all to maintain data integrity. Transactions follow the ACID properties:
- Atomicity – Ensures all steps succeed or the transaction is rolled back.
- Consistency – Keeps the database in a valid state before and after execution.
- Isolation – Ensures transactions don’t interfere with each other.
- Durability – Guarantees changes persist even after a system failure.
Example: When processing Amazon payments, transactions ensure that money is deducted from the buyer and credited to the seller without errors.
This topic is critical in Amazon SQL interview questions because data integrity is a top priority in large-scale databases.
16. What is the Difference Between VARCHAR and CHAR in SQL?
Both VARCHAR and CHAR store string data but differ in storage and performance:
- CHAR – Fixed-length storage (CHAR(10) always stores 10 characters).
- VARCHAR – Variable-length storage (VARCHAR(10) stores only used characters).
For example, Amazon may use CHAR for country codes (CHAR(3)) since they always have three characters, while VARCHAR is used for product descriptions, which vary in length.
In Amazon SQL interviews, expect questions on storage optimization and when to use CHAR vs. VARCHAR for performance tuning.
17. What is the Difference Between DISTINCT and GROUP BY?
Both are used for eliminating duplicates, but they function differently:
- DISTINCT removes duplicate rows from a result set.
- GROUP BY categorizes data and allows aggregations (e.g., SUM, COUNT).
Example: To find unique Amazon customers, use DISTINCT. To group total sales per country, use GROUP BY.
This is a frequent topic in Amazon SQL technical interviews, especially for data analysis and reporting roles.
18. What is a Common Table Expression (CTE) in SQL?
A Common Table Expression (CTE) is a temporary result set that improves query readability and performance.
Benefits:
- Enhances code clarity – Makes queries easier to debug.
- Supports recursion – Useful for hierarchical data.
- Optimizes performance – Can be used instead of subqueries.
For example, Amazon may use a CTE to find top-selling products by precomputing rankings before applying further analysis.
CTEs are often tested in Amazon SQL interview questions, especially in scenarios requiring data transformation and analytics.
19. What is the Difference Between RANK(), DENSE_RANK(), and ROW_NUMBER()?
These window functions assign rankings in ordered result sets:
- RANK() – Assigns ranks with gaps (1, 2, 2, 4).
- DENSE_RANK() – Assigns ranks without gaps (1, 2, 2, 3).
- ROW_NUMBER() – Assigns unique row numbers regardless of duplicates.
Example: In an Amazon sales leaderboard,
- RANK() allows ties but skips numbers.
- DENSE_RANK() does not skip numbers.
- ROW_NUMBER() ensures a unique order.
Understanding these functions is crucial for Amazon SQL interviews, especially in data ranking and analytics tasks.
20. What is Data Warehousing in SQL? How is it Used in Amazon?
A Data Warehouse is a specialized system for storing, analyzing, and retrieving large datasets.
Amazon uses data warehousing for:
- Customer behavior analysis – Powering Amazon’s recommendation engine.
- Business intelligence – Tracking sales, profits, and trends.
- Performance optimization – Using Amazon Redshift for fast SQL queries.
A solid understanding of data warehousing is essential in Amazon SQL interviews, especially for data engineering and business intelligence roles.
21. What is Indexing in SQL, and Why is It Important?
An Index in SQL is a database object that improves query performance by speeding up data retrieval. Instead of scanning the entire table, the database uses the index to locate rows efficiently.
There are several types of indexes:
- Clustered Index – Sorts and stores data physically based on indexed columns.
- Non-Clustered Index – Creates a separate structure for quick lookups.
- Composite Index – Includes multiple columns for better filtering.
Example: In an Amazon product catalog, an index on the ProductID column speeds up search queries, making it faster to retrieve specific product details.
Since Amazon handles massive databases, indexing is crucial for query optimization. Candidates should be familiar with indexing strategies as they are frequently tested in Amazon SQL interview questions.
22. What is the Difference Between INNER JOIN and OUTER JOIN?
JOINS are used to combine data from multiple tables based on related columns.
- INNER JOIN – Returns only matching rows from both tables.
- OUTER JOIN – Returns all rows from one or both tables, even if there’s no match.
- LEFT JOIN – Returns all records from the left table and matching records from the right.
- RIGHT JOIN – Returns all records from the right table and matching records from the left.
- FULL JOIN – Returns all records when there is a match in either table.
Example: In an Amazon order management system, an INNER JOIN between Orders and Customers retrieves only customers who have placed an order. A LEFT JOIN shows all customers, even those without orders.
Understanding JOIN operations is crucial for Amazon SQL interview preparation, as many real-world datasets are relational.
23. What is SQL Normalization? Why is It Important?
SQL Normalization is the process of structuring databases to reduce redundancy and improve data integrity. It organizes data into multiple related tables to avoid duplicate information.
Normalization follows multiple forms:
- 1NF (First Normal Form) – Ensures atomicity (each column contains unique values).
- 2NF (Second Normal Form) – Removes partial dependencies.
- 3NF (Third Normal Form) – Eliminates transitive dependencies.
- BCNF (Boyce-Codd Normal Form) – Ensures stricter functional dependencies.
Example: Amazon may use normalization to store customer details separately from order history. This prevents data duplication and ensures updates are more efficient.
Normalization is a frequently tested topic in Amazon SQL interviews, particularly for database optimization and data integrity scenarios.
24. What is Denormalization in SQL? When Should It Be Used?
Denormalization is the opposite of normalization, where data is combined into fewer tables to improve query performance at the cost of some redundancy.
Advantages:
- Faster query performance – Reduces the need for complex JOINs.
- Optimized for read-heavy operations – Ideal for data warehousing.
- Better scalability – Useful in big data environments.
Example: In Amazon’s big data analytics, a denormalized table might combine customer orders and product details into one table for faster reporting instead of joining multiple tables.
Amazon SQL interview questions often test when to use normalization vs. denormalization, as both approaches are essential in real-world database architecture.
25. What Are Stored Procedures in SQL? Why Are They Used?
A Stored Procedure is a precompiled collection of SQL statements stored in the database and executed as a unit.
Advantages:
- Improves performance – Reduces repeated SQL parsing.
- Enhances security – Limits direct table access.
- Encapsulates business logic – Ensures consistency in operations.
Example: Amazon may use a stored procedure to process bulk orders efficiently, ensuring that inventory is updated and payments are processed atomically.
Stored procedures are common in Amazon SQL interview preparation, especially for automation and performance optimization tasks.
