• TL;DR: How to solve algorithmic problem?
• How will you be evaluated during a coding interview?
• My step-by-step plan for algo problem solving
• During the Interview
• Questions: Corner Cases and Constraints
• Tricks and Advice
• 1 Day Before Interview
• Reverse Interview Questions
• More resources

TL;DR: How to solve algorithmic problem?

Read, Repeat, Understand inputs & output with examples, Constraints (edge cases), Ideas (whys, tradeoffs, solve simpler version, imagine helper functions - go from high level to low level), Complexities, Code, Test code. Interact more.

How will you be evaluated during a coding interview?

1. Communication - Asking clarifying questions, communication of approach and tradeoffs clearly such that the interviewer has no trouble following.
2. Problem solving - Understanding the problem and approaching it systemically, logically and accurately, discussing multiple potential approaches and tradeoffs. Ability to accurately determine time and space complexity and optimize them.
3. Technical competency - Translating discussed solutions to working code with no significant struggle. Clean, correct implementation with strong knowledge of language constructs.
4. Testing - Ability to test code against normal and corner cases, self-correcting issues in code.

Check Untitled

My step-by-step plan for algo problem solving

Track time, overall 45 minutes (5 minutes to understand problem, 10 minutes to propose solution, 15 minutes for code, 10 minutes for follow-up question, 5 minutes for open talk)

1. Read the problem, repeat it, understand it. Don’t immediately jump into coding!
2. Understand inputs and outputs. Draw some examples. Clarify requirements, ask clarifying questions, and understand constraints (edge cases). Example questions: Is it ASCII or Unicode? What is the max value? Is there a difference between capital letters and small letters? Check my questions lists here.
3. Think about the solution in your mind. Divide problems into sub-problems. Come up with different ideas (ask whys, think about trade-offs, solve simpler versions, imagine helper functions - go from high level to low level).
4. Evaluate the complexity and trade-offs.
5. Think of a better alternative solution.
6. Debug your code on paper and test with new corner case inputs.
7. Write code. Write clean code.
8. Write tests. Positive, negative, with edge-cases.

More to read:

• HiredInTech Algorithm Design Canvas

During the Interview

1. Make a good self introduction at the start of the interview, 1 — 2 min max. Speak with a smile and you will naturally sound more engaging.
2. Ask how many problems we will solve? It will help you with time management during interview.
3. Track time! 45 minutes (5 minutes to understand problem, 10 minutes to propose solution, 15 minutes for code, 10 minutes for follow-up question, 5 minutes for open talk)
4. Follow my step-by-step plan:
• Paraphrase and repeat the question back at the interviewer to make sure you understand the problem right, and to solve the correct problem.
• Clarify assumptions, ask questions (input, output, problem specific, data structure specific questions)
• Run through a simple example, propose a solution, provide complexity analysis, only code if interviewers says OK.
• and so on
5. Do not jump into coding right away or before the interviewer gives you the green light to do so.
6. Communication and collaboration, problem solving and critical thinking.
1. These conversations are meant to be collaborative and show you what it would be like to work with us. Talk like you are talking to friend. I am a problem solver, I am a collaborative team member, take ownership of projects.
2. Interview is about evaluating how you think and approach to solution, not about writing 100% working code. Communication/collaboration of thought process and problem solving ability.
‣
Tips: Explain and clarify, clarify questions. Keep thinking.
1. Explain and clarify! (show your thought process, think aloud, why I am doing so, reason, tradeoff)
1. Your thought process and problem-solving ability
2. Explain like you teach someone.
2. Clarify questions! (show that you can ask good questions to get requirements)
1. Understanding the problem, requirements, corner cases
3. Keep thinking! (how to improve the solution you have provided)
1. Write agile code, easy to change if new follow-ups will arise. Adapt to new parameters.
2. Time and space complexity, will your solution scale?
4. Your ability to listen and collaborate
5. Do not appear unsure about your approach or analysis
7. During writing code:
1. Explain what you are trying to achieve as you are coding
2. Write actual compilable, working code where possible, not pseudocode
3. Write clean code, use good variables names (don’t use a, b, c)
4. Ask for permission to use trivial functions without having to implement them. E.g. reduce, filter, min, max should all be ok to use
5. Write simple assert test and run simple example
6. Explain trade-offs and how the code / approach can be improved if given more time
8. Ask from interviewers if you are going right direction.
9. Do not interrupt your interviewer when they are talking. Usually if they speak, they are trying to give you hints or steer you in the right direction.
10. Do not use a lot of programming language built-in functions. If you use it, describe what the method does.
11. If stuck: solve simpler version of problem. It is okay to have some code, then optimize it.

For example, you need to get a sorted data structure for the problem. You can ask the interviewer if it is okay to create imaginary function.

12. Make it feel like you're on a team. Think aloud.
1. Should I handle this case?
2. Is it ok I will make this assumption?
3. Let's try doing it this way—not sure yet if it'll work.
4. Then I'd usually check the code against some edge cases—should we do that next?
13. Optimizations: Try different Data Structure: Array, HashMap/Set, Heap, Queue, Stack. LinkedList. Tree. Graph. Trie.
14. In the end of the interview ask 2 questions about company, prepare them beforehand.

Questions: Corner Cases and Constraints

1. Ask about edge cases (propose edge case). Explain and clarify, DS tradeoff, time and space complexity.
2. Questions
1. How am I receiving this data?
2. How should I output the result?
3. What if wrong input is provided?

How long/big our input could be?

Empty inputs, null values, 0 length, 1 element input, super long input.

3. Is size, speed, using not build-in library a concern?
4. How I would be testing? Tests: Zero, one, two, two to max-1, max, max+1
5. Questions to ask
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OOP
• how many call will we get for each?
• Do we need caching if similar calls or similar work needs to be done?
• what if the data object is empty before calling the remove/pop/top methods?
‣
Number (int, float)
1. what if we have floats?
2. zero?
3. Can we have negative/positive numbers?
4. what is the range on integer? max and min integer?
5. dividing by zero?
6. do we have leading 0s?
7. Can we use bit manipulation? XOR?
‣
Strings
1. ASCII, Unicode UTF-8 (special chars)? Character encoding standards. ProTip™, 👻, 60%, https://google.com
2. Only lower-case English characters, or? How to handle them?
3. what if the string is empty, “ “, NULL, length is 1?
4. spaces in string in the beginning or in the end?
5. max and min length of string?
6. sorted? can we sort it?
7. DS: Can we use stack, queue, heap? If duplicates maybe use stack?
‣
Array
1. Mutable or immutable array?
2. Should it be in place? Or we need to return new one?
3. Items in array are all unique?
4. Do we have repeated items in array? OR can I use the same element twice?
5. Does array contain NULLs?
6. How to handle an empty array?
7. What would I do if the array is super large.
8. Can we sort the array?
9. Do we need to preserve ORDER?
‣
Sorting algorithm
1. empty input, null input
2. 1 element, very long input
3. duplicate elements (sort on a second condition?)
4. odd/even length input
5. Collection with all elements equal?
6. Garbage inside the collection?
‣
Stack/queue
1. removing elements from empty stack/queue
‣
Linked list
1. Single linked list or doubly linked list?
2. Is it a circular linked list?
3. How many nodes does the linked list contain?
4. Null values for head/root
5. Can it be empty?
6. Values are sorted? Are there any duplicate numbers?
7. Can I convert it to array?
‣
Tree
Copy
1. Edge = link between any two nodes.
2. Height of node = number of edges from the deepest leaf to node.
3. Depth of a node = number of edges from the root to the node.
4. Height of a tree = height of the root node.
1. Is it a Binary Tree? (Each node has at most two children.)
2. Is it a Binary Search Tree (BST)? (Left child is less than the parent, and right child is greater.)
3. If BST do we have duplicates? And in which side?
4. Height of the tree? (Longest path from a leaf node to root.)
5. Depth of a specific node? (Length of the path from the root to that node.)
6. Is it a Balanced Tree? (Height of left and right subtrees differ by at most 1. Ensuring O(logn) for insert and find)
7. Is it a Complete Binary Tree? (All levels are filled, except possibly the last one, and nodes are left-justified.)
8. Is it a Full Binary Tree? (Each node has either 0 or 2 children.)
9. Is it a Perfect Binary Tree? (All internal nodes have two children, and all leaves are at the same level.)
10. Is it a Binary Heap? (Specifically for Binary Trees that follow the heap property.)
11. Is it a Symmetric Tree (or Mirror Tree)? (The left subtree of one node is a mirror image of the right subtree of the other node.)
‣
Graph
• Directed (one-way street) or undirected (two-way)?
• Connected graph(undirected graph, there is always a path to the node) or not-connected?
• Strongly connected graph (directed graph, when there is always a route)
• Cyclic or Acyclic? (Does the graph contain any cycles, or is it a directed acyclic graph (DAG)?)
• Weighted or Unweighted? (Are there numerical values assigned to the edges?)
• Tree or Forest? (Is the graph a connected acyclic graph, or is it a collection of disconnected trees?)
• Complete or Incomplete? (Does every pair of distinct vertices have an edge between them?) From any node (vertex) you can do to any node.
• Should I use DFS or BFS?
• Dijkstra?
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Loops
1. while loops running forever (properly incre./decre. pointers)
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Recursion
1. recursion 1.000 call stack size is input it too big?

Tricks and Advice

• https://www.techinterviewhandbook.org/coding-interview-prep/ (read all)
• Interview Cake: Programming Interview Questions and Tips Tricks For Getting Unstuck During a Coding Interview | Interview Cake
• Interview Cake: Programming Interview Questions and Tips Coding Interview Advice | Interview Cake
• Azim Pulat 100ta Intervyu Nima O'rgatdi?
• https://www.techinterviewhandbook.org/coding-interview-prep/

1 Day Before Interview

• Interview Cake: Programming Interview Questions and Tips The 24 Hours Before Your Interview | Interview Cake

Reverse Interview Questions

1. What’s you favorite part of working at ___?
2. What does it mean to be successful here, and how do you measure success?
3. What would a typical day/week for someone in this role look like? Meetings, working, meetings?
4. How the first few month look like at … when you join the team?
5. What are the engineering challenges that the company/team is facing?
6. How are requirements / expectations communicated? What is the performance review process like?
7. How do you test your apps? How releases look like at …? Code review process? Test plan?
8. What do you like least? What would you change if you could?
9. How do you look at interviews, when tools such copilot or GPT come out?

More questions: https://github.com/yangshun/tech-interview-handbook/blob/main/apps/website/contents/final-questions.md

More resources

GitHub - Rustam-Z/cracking-maang: 🥷 Cracking the MAANG Interviews | Google & Bloomberg | Algorithms and Data Structures | Tips and Resources

🥷 Cracking the MAANG Interviews | Google & Bloomberg | Algorithms and Data Structures | Tips and Resources - GitHub - Rustam-Z/cracking-maang: 🥷 Cracking the MAANG Interviews | Google &am...

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