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Microsoft·Software Engineer·Online Assessment (OA)·Junior

JuniorPending
Sep 2026Remote

Summary

Applied for a Microsoft SWE summer internship and just wrapped up the OA. Two coding problems, one manageable and one that exposed some gaps in my algorithms prep. Now sitting here wondering if partial credit is enough to move forward.

Questions Asked (2)

Q1

Solve a coding problem at roughly easy difficulty level, passing all test cases.

Algorithms & Data Structures
Author's notes

Got through this one clean.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then walk through a simple example to confirm understanding. Choose the most straightforward algorithm that meets the time and space requirements, and implement it cleanly with meaningful variable names. Test your solution with the provided examples and additional edge cases before finalizing.

Pro tip: Verbalize your thought process clearly and check in with the interviewer after outlining your approach—this demonstrates collaboration and gives you a chance to catch misunderstandings early. Also, always consider edge cases like empty inputs or large values, as Microsoft interviewers often value robustness over cleverness.

1. Understand the problem

Restate the problem in your own words and ask clarifying questions about input ranges, expected output, and constraints. Confirm with the interviewer before proceeding.

2. Explore examples

Walk through a simple example manually to verify your understanding and identify edge cases. This helps you spot patterns and potential pitfalls early.

3. Design an approach

Outline a high-level algorithm, mentioning time and space complexity. If multiple solutions exist, briefly compare them and justify your choice.

4. Implement the solution

Write clean, modular code with clear variable names. Explain your logic as you code, and handle edge cases explicitly.

5. Test and refine

Run through the provided examples and additional edge cases. If time permits, discuss potential optimizations or alternative approaches.

Key Points to Mention

  • Time and space complexity analysis of your solution
  • Edge cases such as empty input, single element, or large values
  • Trade-offs between different approaches (e.g., iterative vs. recursive)
  • Code readability and maintainability (naming, structure)
  • Testing strategy including unit tests or manual walkthroughs
  • Communication of thought process and collaboration with interviewer

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Solve a harder coding problem requiring you to select the right data structure under time constraints.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where things fell apart.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then verbally compare two or three candidate data structures with their time/space trade-offs. Choose the one that best balances expected performance and implementation simplicity, and explain your reasoning before coding.

Pro tip: Interviewers care more about your thought process than a perfect solution—narrate your trade-off analysis and mention how you'd test and optimize if given more time.

1. Clarify requirements and constraints

Ask about input size, expected time/space limits, and edge cases to narrow down viable data structures.

2. Brainstorm candidate data structures

List 2-3 options (e.g., hash map, heap, trie) and quickly assess their time complexity for the core operations.

3. Select and justify your choice

Pick the structure that best meets the constraints, explicitly stating why others are less suitable.

4. Outline the algorithm and code

Sketch the steps using the chosen structure, then implement cleanly while explaining your logic.

5. Test and discuss optimizations

Walk through edge cases, verify complexity, and mention potential improvements or alternative approaches.

Key Points to Mention

  • Time and space complexity analysis of each candidate data structure
  • Trade-offs between different approaches (e.g., hash map vs. balanced BST)
  • Edge cases and how the chosen structure handles them
  • Scalability and performance under large inputs
  • Readability and maintainability of the code
  • Testing strategy and potential optimizations

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.