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Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

Here you can find essential SQL Interview Resources👇
https://t.iss.one/mysqldata

Like this post if you need more 👍❤️

Hope it helps :)

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We’ve officially hit 2.5 Million followers — and it’s time to level up together! ❤️

I’m launching a Python Projects Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step, hands-on journey — where you’ll build useful Python projects with clear code, explanations, and mini-quizzes!

Here’s what we’ll cover:

🔹 Week 1: Python Mini Projects (Daily Practice)
⦁ Calculator
⦁ To-Do List (CLI)
⦁ Number Guessing Game
⦁ Unit Converter
⦁ Digital Clock

🔹 Week 2: Data Handling & APIs
⦁ Read/Write CSV & Excel files
⦁ JSON parsing
⦁ API Calls using Requests
⦁ Weather App using OpenWeather API
⦁ Currency Converter using Real-time API

🔹 Week 3: Automation with Python
⦁ File Organizer Script
⦁ Email Sender
⦁ WhatsApp Automation
⦁ PDF Merger
⦁ Excel Report Generator

🔹 Week 4: Data Analysis with Pandas & Matplotlib
⦁ Load & Clean CSV
⦁ Data Aggregation
⦁ Data Visualization
⦁ Trend Analysis
⦁ Dashboard Basics

🔹 Week 5: AI & ML Projects (Beginner Friendly)
⦁ Predict House Prices
⦁ Email Spam Classifier
⦁ Sentiment Analysis
⦁ Image Classification (Intro)
⦁ Basic Chatbot

📌 Each project includes: 
✅ Problem Statement 
✅ Code with explanation 
✅ Sample input/output 
✅ Learning outcome 
✅ Mini quiz

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Questions & Answers for Data Analyst Interview

Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.

Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.

Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.

Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.

Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.