JB Poindexter (India) Private Limited is hiring for Jr Pricing Analyst
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American Express is hiring for Software Engineer I
Experience: 0-2 years
Salary: 15-25 LPA

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Gainwell Technologies
Position: C Unix Developer
Qualification: Bachelor’s Degree
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Priceline is hiring for Associate Software Engineer
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Artpark is hiring for Data quality check executive (Freelance)
Batch: 2023, 2024, 2025, 2026

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Octave is hiring for Software Engineer
Batch: 2021, 2022, 2023, 2024

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American Express is hiring for Software Engineer I
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✅ SQL Interview Questions with Answers

1. What is a window function? 
A window function computes results over a group ("window") of rows related to the current row, without collapsing them (like GROUP BY).

Examples: ROW_NUMBER(), RANK(), SUM() OVER(...) for running totals, rankings, or moving averages.

2. What is the difference between RANK() and ROW_NUMBER()? 
• ROW_NUMBER(): assigns unique sequential numbers to all rows, even if values are equal.
• RANK(): gives same rank to tied values, then skips the next rank (e.g., 1, 1, 3).

3. How do you find the second highest salary? 
SELECT salary 
FROM ( 
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk 
  FROM employees 
) t 
WHERE rnk = 2; 
This avoids ties if you want exactly the second‑highest value.

4. What is a recursive CTE? 
A recursive CTE refers to itself in its WITH definition, usually in the form "anchor + UNION ALL recursive step". It is used for hierarchical data like managers‑employees, org charts, or tree structures.

5. What is the difference between correlated and non-correlated subquery? 
• Non‑correlated: runs once, independent of the outer query.
• Correlated: references columns from the outer query and runs once per outer row (e.g., SELECT ... FROM t1 WHERE col > (SELECT AVG(col) FROM t2 WHERE t2.id = t1.id)).

6. How do you remove duplicates without DISTINCT? 
Use window functions: 
DELETE FROM ( 
  SELECT ROW_NUMBER() OVER (PARTITION BY col1, col2 ORDER BY id) as rn 
  FROM table 
) t 
WHERE rn > 1; 
Or use GROUP BY and keep one row per group.

7. What is an INDEX and when do you use it? 
An index speeds up data retrieval on specified columns (used in WHERE, JOIN, ORDER BY). Use it on columns that are frequently filtered or joined; avoid on very small tables or columns updated often.

8. Explain self-join with example. 
A self‑join joins a table to itself using aliases. Example: 
SELECT e1.name as employee, e2.name as manager 
FROM employees e1 
LEFT JOIN employees e2 ON e1.manager_id = e2.id; 
Useful for parent‑child relationships.

9. What is the difference between DELETE, DROP, and TRUNCATE? 
• DELETE: removes rows (can be filtered by WHERE), can be rolled back.
• TRUNCATE: removes all rows quickly, resets storage; often not logged per row.
• DROP: removes entire table (structure + data); cannot be rolled back.

10. How do you pivot/unpivot data in SQL? 
• Pivot: turns rows into columns (e.g., sales per month as columns) using PIVOT or conditional aggregation (MAX(CASE WHEN ... END)).
• Unpivot: turns columns into rows (e.g., multiple month columns → one month column) using UNPIVOT or UNION ALL/VALUES.

11. What is LAG() and LEAD()? 
• LAG(col, n): value of col from n rows before current row.
• LEAD(col, n): value from n rows after. Used for time‑series analysis (MoM change, prior/next values).

12. How do you handle NULL in aggregates? 
Most aggregates (SUM, AVG, MAX, MIN) ignore NULL. 
• COUNT(col) ignores NULL; COUNT(*) counts all rows.
• Use COALESCE() or ISNULL() to replace NULL before aggregating.

13. What is the difference between VIEW and MATERIALIZED VIEW? 
• VIEW: virtual table; query runs every time you select.
• MATERIALIZED VIEW: stores result physically and refreshes periodically; faster reads, slower updates.

14. Explain ACID properties. 
• Atomicity: transaction is "all or nothing".
• Consistency: valid state before and after.
• Isolation: concurrent transactions don't interfere.
• Durability: committed changes survive crashes.

indexes on WHERE, JOIN, ORDER BY columns.


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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems



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