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Coding Interview Preparation
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Coding interview preparation for software engineers Daily interview questions, algorithms, data structures & clean solutions. Real interview tasks and problems. Join 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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Channel Posts
⭐️ Behavioral: Nail Any “Tell Me About a Time” Question
Use STAR method (Amazon/Google favorite):
Situation: Short context
Task: Your responsibility
Action: What you did (focus here)
Result: Quantify outcome (numbers = gold)
Example Question:
Tell me about a challenging project.❌ Bad: Ramble story. ✅Good: 60-sec STAR answer ending with “...resulted in 25% faster processing.” 👉 Pro Move: Prepare 3-4 stories (failure, leadership, teamwork, conflict). Practice out loud. End with what you learned. This decides “culture fit”, prepare as hard as coding!
| 2 | SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
@coding_interview_preparation | 161 |
| 3 | 🌳 DFS vs BFS : Choose Right in 10 Seconds
👉 DFS (Stack/Recursion): Goes deep first. Great for path existence, cycle detection, or "any valid path".
👉 BFS (Queue): Level by level. Best for shortest path in unweighted graph or "minimum steps".
🗯 Interview Encounter: "Number of islands" or "Shortest path in maze" → BFS wins. "Validate BST" or "Clone graph" → DFS is natural.
✅ Pro Tip: Tell interviewer: “I’ll use BFS for shortest, DFS for space efficiency.” Always mention visited set to avoid cycles. Dry-run small example verbally. | 172 |
| 4 | ✅ Top JavaScript Interview Questions & Answers 💻
📍 1. What is JavaScript and why is it important?
Answer: JavaScript is a dynamic, interpreted programming language that makes web pages interactive. It runs in browsers and on servers (Node.js), enabling features like animations, form validation, and API calls.
📍 2. Explain the difference between var, let, and const.
Answer: var has function scope and is hoisted; let and const have block scope. const defines constants and cannot be reassigned.
📍 3. What are closures in JavaScript?
Answer: Closures occur when a function remembers and accesses variables from its outer scope even after that outer function has finished executing.
📍 4. What is the Event Loop?
Answer: The Event Loop manages asynchronous callbacks by pulling tasks from the callback queue and executing them after the call stack is empty, enabling non-blocking code.
📍 5. What are Promises and how do they help?
Answer: Promises represent the eventual completion or failure of an asynchronous operation, allowing cleaner async code with .then(), .catch(), and async/await.
📍 6. Explain 'this' keyword in JavaScript.
Answer: this refers to the context object in which the current function is executed — it varies in global, object, class, or arrow function contexts.
📍 7. What is prototypal inheritance?
Answer: Objects inherit properties and methods from a prototype object, allowing reuse and shared behavior in JavaScript.
📍 8. Difference between == and === operators?
Answer: == compares values after type coercion; === compares both value and type strictly.
📍 9. How do you handle errors in JavaScript?
Answer: Using try...catch blocks for synchronous code and .catch() or try-catch with async/await for asynchronous errors.
📍 🔟 What are modules in JavaScript and their benefits?
Answer: Modules split code into reusable files with import and export. They improve maintainability and scope management.
💡 Pro Tip: Complement your answers with simple code snippets and real project scenarios if/when possible. | 185 |
| 5 | 🛠 Two Pointers Pattern: Spot It & Solve Fast
When to use: Sorted arrays, find pairs, or remove duplicates.
Simple Template:
left, right = 0, len(arr)-1
while left < right:
if condition(arr[left], arr[right]):
# found or move both
left += 1
right -= 1
elif too_small:
left += 1
else:
right -= 1
Interviewer:
Tell me if two numbers in a sorted array sum to target" (Two Sum II) or "Container With Most Water.
How to Answer:
Array is sorted, so two pointers from ends should work in O(n)
Show brute force first, then optimize. Practice: 3Sum, Remove Duplicates.
🔥 You will solve these in <15 mins next interview! | 242 |
| 6 | A developer changes a single function and unexpectedly breaks five unrelated features. Which design issue is MOST likely present? | 269 |
| 7 | Top 50 Python Interview Questions And Answers.pdf | 242 |
| 8 | 🔥 Coding Interview Acronyms You MUST Know 💻
DSA → Data Structures & Algorithms
CPU → Central Processing Unit
RAM → Random Access Memory
DBMS → Database Management System
RDBMS → Relational Database Management System
ACID → Atomicity, Consistency, Isolation, Durability
OLTP → Online Transaction Processing
OLAP → Online Analytical Processing
TCP → Transmission Control Protocol
IP → Internet Protocol
DNS → Domain Name System
MVC → Model View Controller
MVVM → Model View ViewModel
SDLC → Software Development Life Cycle
CI/CD → Continuous Integration / Continuous Deployment
JWT → JSON Web Token
ORM → Object Relational Mapping
API → Application Programming Interface
REST → Representational State Transfer
SOAP → Simple Object Access Protocol
Big O → Time & Space Complexity Notation
FIFO → First In First Out
LIFO → Last In First Out
@coding_interview_preparation | 316 |
| 9 | Which traversal of a Binary Search Tree produces sorted output? | 299 |
| 10 | Which traversal of a Binary Search Tree produces sorted output? | 1 |
| 11 | ✅ 90 Data Science Interview Questions
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics? | 318 |
| 12 | 🔥 Dynamic Programming (DP)
Dynamic Programming is one of the most important and slightly advanced topics in coding interviews.
📌 What is Dynamic Programming?
Dynamic Programming is a technique used to solve complex problems by breaking them into smaller subproblems and storing their results.
👉 Instead of solving the same problem again and again, we reuse previously computed results.
🧠 Why DP is Needed?
Some problems have:
• Overlapping subproblems (same calculation repeated)
• Optimal substructure (solution built from smaller solutions)
DP helps to:
• reduce time complexity
• avoid redundant calculations
⚙️ Two Approaches in DP
1️⃣ Memoization (Top-Down)
Uses recursion
Stores results in memory (cache)
Avoids repeated calculations
👉 Think: solve first, store later
2️⃣ Tabulation (Bottom-Up)
Uses iteration
Builds solution step by step
No recursion
👉 Think: build from smallest to largest
🔁 Example Concept: Fibonacci
Normal recursion:
Repeats same calculations → slow
Dynamic Programming:
Store results → faster
👉 This reduces complexity from O(2ⁿ) to O(n)
🧠 Key DP Patterns
1️⃣ 1D DP
Example:
• Fibonacci
• Climbing stairs
2️⃣ 2D DP
Example:
• Grid problems
• Longest Common Subsequence
3️⃣ Knapsack Pattern
Example:
• Max value with limited weight
4️⃣ Subsequence Problems
Example:
• Longest Increasing Subsequence
⚡️ When to Use DP
Look for:
• Repeated subproblems
• Need for optimization
• Recursive solution possible
• “Find maximum/minimum ways”
⚠️ Common Mistakes
❌ Not identifying overlapping subproblems
❌ Using recursion without memoization
❌ Wrong state definition
❌ Not understanding transitions
🎯 Interview Questions
• What is Dynamic Programming?
• Difference between DP and recursion
• Memoization vs Tabulation
• Fibonacci using DP
• Knapsack problem
• Longest Common Subsequence
⭐️The Main Point:
DP is not about memorizing problems.
It’s about identifying patterns like:
👉 “Can I reuse previous results?”
💡 Simple Thought Process
1. Can I break problem into smaller parts?
2. Are subproblems repeating?
3. Can I store results?
👉 If yes → Use DP
@coding_interview_preparation | 339 |
| 13 | React js Interview.pdf | 731 |
| 14 | Which data structure would you choose to implement an autocomplete feature? | 406 |
| 15 | Don't overwhelm yourself to learn Git
Git is only this much👇😇
1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull
2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick
3.Merging:
• git merge
• git rebase
4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop
5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror
6.Configuration:
• git config
• git global config
• git reset config
7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref
8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag
9.Alias:
• git config --global alias.<alias> <command>
10.Hook:
• git config --local core.hooksPath <path>
@coding_interview_preparation | 495 |
| 16 | ▎ Algorithms - Quick Reference Cheat Sheet
In this post, we’ll cover some fundamental algorithms that every programmer should know.
📌 Sorting Algorithms
Sorting is essential for organizing data. The most common sorting algorithms include:
• Bubble Sort: A simple comparison-based algorithm with a time complexity of O(n²). It repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order.
• Selection Sort: This algorithm divides the input list into two parts: a sorted and an unsorted region. It has a time complexity of O(n²) as well, selecting the smallest (or largest) element from the unsorted part and moving it to the sorted part.
• Insertion Sort: Builds the final sorted array one item at a time. It has a time complexity of O(n²) but performs well for small data sets or nearly sorted data.
• Merge Sort: A divide-and-conquer algorithm with a time complexity of O(n log n). It divides the array into halves, sorts them, and merges them back together.
• Quick Sort: Another divide-and-conquer algorithm with an average time complexity of O(n log n). It selects a 'pivot' element and partitions the other elements into two sub-arrays according to whether they are less than or greater than the pivot.
📌 Search Algorithms
Searching is crucial for finding elements in data structures. Key search algorithms include:
• Linear Search: A simple method with a time complexity of O(n) that checks each element in a list until it finds the target value.
• Binary Search: A more efficient search method with a time complexity of O(log n), but it requires the list to be sorted. It repeatedly divides the search interval in half.
Graph Algorithms: Graphs are used to represent networks. Important graph algorithms include:
• Depth-First Search (DFS): Explores as far as possible along each branch before backtracking. It's implemented using recursion or a stack.
• Breadth-First Search (BFS): Explores all neighbors at the present depth prior to moving on to nodes at the next depth level. It's implemented using a queue.
Dynamic Programming: This technique is used to solve problems by breaking them down into simpler subproblems and storing the results to avoid redundant calculations. Common examples include the Fibonacci sequence and the Knapsack problem.
📝 Tips for Interviews:
👉 Understand how different algorithms work and their time/space complexities.
👉 Be prepared to explain your reasoning behind choosing a specific algorithm for a problem.
👉 Practice coding these algorithms from scratch to reinforce your understanding. | 504 |
| 17 | 140_python_exercises.pdf | 484 |
| 18 | Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf | 2 015 |
| 19 |  Microservices Best Practices | 667 |
| 20 | 400+ javascript questions .pdf | 603 |
