Ace Your SQL Interview: Company-Wise Questions You Can’t Miss!! [Part ]

Picking up where I left off with my company-wise questions, Check out the link to Part 1 at the end of this post.

1. American Express SQL Interview Question and Solution | Page Recommendation
   https://lnkd.in/g_sMN26m

2. Fractal Analytics SQL Interview Question (Game of Thrones Database) | SQL for Data Engineer
   https://lnkd.in/gGcsBms5

3. Netflix Data Cleaning and Analysis Project | End to End Data Engineering Project (SQL + Python)
   https://lnkd.in/gS8mT7Fn

4. Swiggy Data Analyst SQL Interview Question and Answer
   https://lnkd.in/gSyhmmhd

5. Cracked Myntra as Data Analyst with 1 Year Experience
   https://lnkd.in/gekpAit8

6. PwC SQL Interview Question for a Data Analyst Position | SQL For Analytics
   https://lnkd.in/gyD5Pjny

7. PayPal Data Engineer SQL Interview Question (and a secret time-saving trick)
   https://lnkd.in/gAJ_Ug79

8. Adobe Interesting SQL Interview Question | Solving Using 2 Approaches | Data Analytics
   https://lnkd.in/gEEAfi8j

9. Walmart Labs SQL Interview Question for Senior Data Analyst Position | Data Analytics
   https://lnkd.in/gRBPb-ms

10. PayPal SQL Interview Problem (Level Hard) | Advanced SQL Problem
    https://lnkd.in/gGZaYt6N

📌 Ace Your SQL Interview: Company-Wise Questions You Can’t Miss! [Part 1]
https://lnkd.in/egZuA-zD

🔗 Join our Data Community to learn, share and grow together!!
https://lnkd.in/gy4R55Tj

You Should Know:

Essential SQL Commands for Interviews

-- Basic SELECT with filtering 
SELECT  FROM employees WHERE department = 'Data Engineering';

-- JOIN operations (INNER, LEFT, RIGHT) 
SELECT e.name, d.department_name 
FROM employees e 
INNER JOIN departments d ON e.dept_id = d.id;

-- Aggregation with GROUP BY 
SELECT department, COUNT() as employee_count 
FROM employees 
GROUP BY department;

-- Window functions (ROW_NUMBER, RANK) 
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) as rank 
FROM employees; 

Data Cleaning in Python (Pandas)

import pandas as pd

Handling missing values 
df = pd.read_csv('data.csv') 
df.fillna(0, inplace=True)

Removing duplicates 
df.drop_duplicates(inplace=True)

String manipulation 
df['name'] = df['name'].str.upper() 

Linux Commands for Data Engineers

 Monitor running processes 
top

Search for a file 
find / -name "data.csv"

Check disk usage 
df -h

Extract data from logs 
grep "ERROR" /var/log/syslog 

Windows PowerShell for Data Tasks

 Export SQL query results to CSV 
Invoke-Sqlcmd -Query "SELECT  FROM employees" -ServerInstance "localhost" | Export-Csv -Path "output.csv"

Schedule a task (for ETL) 
schtasks /create /tn "DailyETL" /tr "C:\scripts\etl.bat" /sc DAILY 

What Undercode Say:

SQL remains a cornerstone for data professionals. Mastering these interview questions ensures readiness for real-world challenges. Combine SQL with Python, Linux, and automation tools (PowerShell, Bash) to streamline workflows. Always validate queries in test environments before production deployment.

Expected Output:

A structured guide for SQL interview prep, supplemented with practical code snippets and system commands for holistic data engineering expertise.

(Note: WhatsApp/Telegram links and non-relevant comments removed.)

References:

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Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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