Amazon, one of the most sought-after and renowned FAANG companies, has a spectrum of opportunities to offer. And like any other tech giant, Amazon also receives a huge amount of applications from around the globe. The journey to get past the competition and land a job at Amazon is challenging.
So, if you are a software engineer, a software developer, a data engineer, an engineering manager, or a tech lead gunning for a job at Amazon, then this article is for you! SQL is an important part of Amazon tech interviews and in this blog, we will explore some of the common Amazon SQL interview questions to help you prepare better.
Amazon has an elaborate interview procedure similar to other FAANG companies. However, with the increasing seniority of the position, the interview process becomes more stringent and gruesome. The entire Amazon SQL interview process can be divided into three stages:
After your profile is shortlisted, phone screening is the preliminary round of the interview process. The questions at the phone interview will be related to your resume and skills. This stage of the Amazon SQL interview process is more focused on understanding the applicant compared to their technical proficiency.
Technical interview questions for Amazon are mostly related to coding, algorithms, and machine learning. You will be assigned 1 or 2 problems to solve within a stipulated amount of time.
If you are planning to apply for any of the data science positions or the positions of business analyst, business intelligence engineer, software engineer, software developer, and tech lead, then a technical interview on SQL will be a part of the process.
The final and most challenging round of the Amazon SQL Interview process is the on-site interview, also known as “The Loop.†There can be 3-5 rounds, where each round goes on for about 45 minutes and is extremely exhausting.
The loop can be further broken down into 3 types of rounds:
1. Coding round: The primary focus of this round is to gauge an idea of the candidate’s technical expertise. You will be assigned 1 or 2 coding problems, and your approach towards solving them will be analyzed.
2. Behavioral round: It is one of the integral rounds of all on-site interviews and is based on Amazon’s 14 leadership principles.
3. The Bar raiser round: This round is the final filter round. At the end of the round, only handpick candidates who pass the assessment of Amazon’s trained recruiters get selected.
Also read: How Hard Is It to Get a Job at Amazon?
This section will serve as your “go-to SQL interview prep guide,†as we have covered the most commonly asked SQL interview questions at Amazon. In addition to getting well-versed in Amazon SQL interview questions, it is also suggested to brush up on your problem-solving skills, as it comes in handy during FAANG interviews.
SQL stands for Structured Query Language. It is sort of like a tool that helps you communicate with a database. For example, imagine having a huge spreadsheet full of information, and you were trying to locate something in particular. You wouldn’t find it by going through the hundreds of pages one by one; you would instead ask a question about the database to get back what you want.
It’s like asking, “Give me all the orders from last week.” SQL facilitates data retrieval or modification in a very fast and efficient manner even if the data becomes humongous. It is also easy to learn because its commands are similar to simple English.
Amazon database is one of the Amazon Web Services, which is a fully managed petabyte-scale data warehouse service. There are various Amazon Database Services available, such as DynamoDB, RDS, ElastiCache, and RedShift.
DynamoDB Auto Scaling is a service feature that automatically scales read and write throughput for your DynamoDB tables based on traffic demands. In other words, if there is a change in the amount of data that needs to be read or written by your application, DynamoDB will automatically scale to meet that change without any manual intervention in the process. In this respect, it helps optimize your costs while ensuring performance through spikes in traffic​.
For collecting and processing E-Commerce data for real-time analysis, I’ll use DynamoDB. DynamoDB is a fully managed NoSQL database service that can be used for any type of unstructured data and can also be used for the data taken from e-commerce websites.
Inner join is the default join, which is used when no join is specified. INNER JOIN in SQL Inner Join returns the matched rows from both tables. It is the join operation that gets only those rows that satisfy the joins condition and discards the rows that do not match.
The full outer join returns all the rows from both tables, inserting NULLs in places where no matching rows exist and matching up the rows where a match can be made. This join is useful if you want to retain all the data from both tables, whether they have corresponding values or not.
The only constraint that works at the column level is NOT NULL. A column cannot have a NULL (empty) value with this constraint, and this would be an important thing if the field always contains some data, like a primary key or mandatory fields like your e-mail or username.
No, a rollback cannot be performed, as ALTER is a DDL command. A DDL command auto commits so once a DDL command is executed the changes become permanent in the database. Thus, there is no way to do a rollback.
Pseudocolumns in SQL are columns that do not exist in the physical database but are there to be referenced like other columns. Examples include ROWNUM, which can give a unique number to every returned row from a query, and USER, which returns the name of the user currently logged into a database.
The SQL command to change the password of a user ‘x’ would be:
ALTER USER x IDENTIFIED BY dfgh
This updates the password to ‘dfgh’ for user ‘x’.
In Amazon Redshift, the leader node helps manage communication between the client applications and the data warehouse. It also manages how to assign tasks as well as execution plans for queries. The compute node executes these tasks, processes data, and returns results to the leader node. Therefore, a leader node ensures efficient interactivity between compute nodes and clients​.
Relational databases, like SQL organize the data into structured tables with defined relationships for increased data security and integrity. These are the databases that follow rigid schemas and work very well for structured data whose shape does not change much.
In contrast to SQL databases, NoSQL databases are non-relational and can process unstructured or semi-structured data like documents or graphs. NoSQL databases are designed to offer more flexibility, they can have schema changes on the fly and tend to perform better when it comes to dealing with larger, distributed data sets.
Amazon Redshift is a fully managed data warehouse service of AWS. Targeted towards big data processing, it allows users to perform a complex query on a huge amount of data with ease. With an MPP query engine at its core, it has the ability to distribute the queries across all nodes which gives significantly better performance. This makes it a great choice for organizations that have large datasets to analyze in real-time.
A stored procedure is a precompiled group of SQL statements and optionally control-of-flow statements stored in a database. You don’t write and then execute one SQL statement, followed by another or else, but you can call that stored procedure, and they’ll get executed all at once.
That improves performance because the SQL code inside the procedure has already been precompiled; hence its execution time is relatively shorter. Stored procedures are commonly used to perform procedures like data validation or complex queries.
SQL indexes are special look-up tables maintained by the database to speed up data retrieval. They do exactly what an index in a book does: they let the database quickly search for rows without having to scan the entire table.
Indexes enhance query performance, mostly in large databases, but they have trade-offs, such as increased storage and slower write operations since the index must be updated with every modification of the data.
Irrespective of what your specialization is, tech interviews always feature some coding problems. We have uploaded numerous solved practice problems on our website for your reference. Here, we have mentioned a few important ones here for your reference:
Here are some SQL query questions to practice for the Amazon SQL interview. Practicing these questions will give you a better idea of different queries and how they can used in practical scenarios.
A few pointers to remember while preparing for SQL interview questions are:
Practicing numerous Amazon SQL interview questions will boost your confidence and improve your preparedness for the technical interview. With Interview Kickstart, you can fast track your interview prep with mock interviews, mentoring sessions, and interview prep material designed by FAANG+ experts.
Led by industry experts (from the likes of Google, Facebook, and LinkedIn), our instructors will help you build a strong foundation in various domains from data engineering and machine learning to Android and iOS development, and a lot more. Interview Kickstart will give you all the tools required to be successful in your career or land your dream job.
You can check out some of the success stories of our alumni who have advanced their careers with the help of Interview Kickstart.
The SQL interviews in Amazon are lengthy and require strenuous efforts. However, the SQL Amazon interview is unavoidable for all data engineers. The interview is divided into three rounds: preliminary phone interview, technical interview, and the loop.
Yes, as behavioral interviews form an integral part of Amazon’s recruitment process. The answer to the behavioral interview questions serves as the key to understanding your personality, social-interaction skills, and teamwork.
Amazon uses DynamoDB, a proprietary NoSQL database, along with Oracle and MySQL for specific purposes, ensuring scalability and performance in product information storage.
The success rate, or the proportion of applicants who get a job offer after the Amazon Loop interview, is 20%, according to Misha Yurchenko, the author of “Cracking the Code.”
In the US, the average annual salary for an Amazon data engineer is around $135,00.
Related readings:
Attend our free webinar to amp up your career and get the salary you deserve.
693+ FAANG insiders created a system so you don’t have to guess anymore!
100% Free — No credit card needed.
Time Zone:
Get your enrollment process started by registering for a Pre-enrollment Webinar with one of our Founders.
The 11 Neural “Power Patterns” For Solving Any FAANG Interview Problem 12.5X Faster Than 99.8% OF Applicants
The 2 “Magic Questions” That Reveal Whether You’re Good Enough To Receive A Lucrative Big Tech Offer
The “Instant Income Multiplier” That 2-3X’s Your Current Tech Salary
The 11 Neural “Power Patterns” For Solving Any FAANG Interview Problem 12.5X Faster Than 99.8% OF Applicants
The 2 “Magic Questions” That Reveal Whether You’re Good Enough To Receive A Lucrative Big Tech Offer
The “Instant Income Multiplier” That 2-3X’s Your Current Tech Salary
Just drop your name and email so we can send your Power Patterns PDF straight to your inbox. No Spam!
By sharing your contact details, you agree to our privacy policy.
Time Zone: Asia/Dhaka
We’ve sent the Power Patterns PDF to your inbox — it should arrive in the next 30 seconds.
📩 Can’t find it? Check your promotions or spam folder — and mark us as safe so you don’t miss future insights.
We’re hosting a private session where FAANG insiders walk through how they actually use these Power Patterns to crack interviews — and what sets top performers apart.
🎯 If you liked the PDF, you’ll love what we’re sharing next.
Time Zone:
