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Amazon Data Analyst Interview Questions for 1-3 years of experience role :-

A. SQL:

1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location

Write an SQL query to find the name of the employee with the highest salary in each location.

2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date

Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.

B. Python:

1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).

2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)

2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.

C. Leadership or Situational Questions:

(Based on the leadership principle of Bias for Action)

- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?

(Based on the leadership principle of Dive Deep)

- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?

(Based on the leadership principle of Customer Obsession)

- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?

D. Excel:

Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.

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Most people learn SQL just enough to pull some data. But if you really understand it, you can analyze massive datasets without touching Excel or Python.

Here are 8 game-changing SQL concepts that will make you a data pro:

👇

1. Stop pulling raw data. Start pulling insights.

The biggest mistake? Running a query that gives you everything and then filtering it later.

Good analysts don’t pull raw data. They shape the data before it even reaches them.

2. “SELECT ” is a rookie move.

Pulling all columns is lazy and slow.

A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights

The more precise your query, the less time you waste cleaning data.

3. GROUP BY is your best friend.

You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month

Grouping turns chaotic data into useful summaries.

4. Joins = Connecting the dots.

Your most important data is split across multiple tables.

Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments

Joins = unlocking hidden insights.

5. Window functions will blow your mind.

They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend

It’s like pivot tables, but way more powerful.

6. CTEs will save you from spaghetti SQL.

Instead of writing a 50-line nested query, break it into steps.

CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable

Good SQL is clean SQL.

7. Indexes = Speed.

If your queries take forever, your database is probably doing unnecessary work.

Indexes help databases find data faster.

If you work with large datasets, this is a game changer.

SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.

Master these 7 concepts, and you’ll never look at SQL the same way again.

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Free Resources to Learn Each Tech Stack 🧠✨

No excuses. Everything you need is free!

1. Frontend Development
❯ freeCodeCamp.org – HTML, CSS, JS
❯ MDN Web Docs – Best docs for web tech
❯ Frontend Mentor – Real-world challenges
❯ CSS Tricks – CSS deep dives
❯ YouTube: Kevin Powell, Web Dev Simplified

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2. Backend Development
❯ Node.js Docs
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❯ Spring Boot Guides
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❯ Google Data Analytics Course
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❯ Google’s ML Crash Course
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Frontend Development Interview Questions

Beginner Level

1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?

Intermediate Level

1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?


Advanced Level

1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?

React ❤️ for the detailed answers

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