← Microsoft Interview Insights

Microsoft·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Microsoft software engineering interview with two algorithmic problems back to back. The binary search one was more interesting than I expected, and the graph question was a grind.

Questions Asked (2)

Q1

You have a sorted list of items. Design an efficient solution using binary search to hit a target objective, rather than going with a dynamic programming knapsack approach.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was knapsack because the problem smelled like an optimization problem.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the problem to ensure binary search is appropriate—specifically, that the data is sorted and the objective is monotonic (e.g., finding a threshold or optimal value). Then, explain how to use binary search to efficiently find the target, contrasting its O(log n) time with the O(nW) or O(n^2) time of dynamic programming. Finally, discuss the trade-offs and when each approach is preferable.

Pro tip: Demonstrate maturity by acknowledging that binary search requires a sorted input and a monotonic predicate; if the problem doesn't meet these, DP might be necessary. Also, mention that binary search can be used on the answer space (binary search on answer) for optimization problems.

1. Clarify the problem

Ask questions to confirm the input is sorted and the objective is to find a target or an optimal value that satisfies a monotonic condition. Ensure binary search is applicable.

2. Define the search space and predicate

Identify the range of possible answers and define a monotonic predicate function that returns true for values that meet the objective and false otherwise.

3. Implement binary search

Write the binary search loop, adjusting the low and high bounds based on the predicate, and handle edge cases like duplicates or no solution.

4. Analyze complexity and trade-offs

Compare the time and space complexity of binary search (O(log n) or O(n log n) if sorting is needed) with the DP knapsack approach (O(nW) or O(n^2)). Discuss when each is appropriate.

5. Test and validate

Walk through examples, including edge cases, to verify the solution works and is indeed more efficient than DP for the given constraints.

Key Points to Mention

  • Binary search requires sorted data or a monotonic predicate; if not, sorting may be needed, adding O(n log n) time.
  • Binary search on answer space: a technique to solve optimization problems by binary searching the answer and checking feasibility.
  • Time complexity: O(log n) for standard binary search, O(n log n) if sorting is required, versus O(nW) for knapsack DP.
  • Space complexity: O(1) for iterative binary search, versus O(nW) for DP.
  • Trade-offs: binary search is faster but only applicable when the problem structure allows; DP is more general but slower.
  • Real-world example: finding the minimum capacity to ship packages within D days (LeetCode 1011) can be solved with binary search on answer.

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

Q2

Given a weighted graph in a custom input format, parse the input to build the graph, then compute the shortest path between two given nodes.

Algorithms & Data StructuresSystem Design
Author's notes

The custom format thing tripped me up more than the actual algorithm.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the input format and edge cases, then outline a parsing strategy that builds an adjacency list. Choose an appropriate shortest path algorithm (Dijkstra for non-negative weights, Bellman-Ford for negative weights) and discuss time/space complexity. Finally, walk through the implementation and test with examples.

Pro tip: Demonstrate awareness of real-world constraints: mention that you'd validate the input format and handle malformed data gracefully, and discuss how the solution scales for large graphs (e.g., using priority queues or early termination).

1. Clarify requirements and constraints

Ask about the input format, graph size, edge weight ranges (negative?), and whether the graph is directed or undirected. Confirm the expected output (path or distance).

2. Design the parsing logic

Describe how to read the custom input: tokenize lines, extract node IDs and weights, and build an adjacency list (or matrix). Mention handling of duplicate edges and self-loops.

3. Select the shortest path algorithm

Choose Dijkstra if weights are non-negative, otherwise Bellman-Ford. Justify the choice based on constraints and discuss time complexity (O(E log V) for Dijkstra with a heap).

4. Implement and optimize

Outline the algorithm steps: initialize distances, use a priority queue, relax edges, and track predecessors for path reconstruction. Mention early exit when the target is reached.

5. Test and validate

Walk through a small example, test edge cases (disconnected graph, unreachable target, negative cycles), and discuss potential optimizations or alternative approaches.

Key Points to Mention

  • Input parsing: tokenization, handling different delimiters, and building an adjacency list
  • Graph representation: adjacency list vs. matrix and trade-offs
  • Algorithm selection: Dijkstra vs. Bellman-Ford based on edge weights
  • Time and space complexity analysis
  • Edge cases: disconnected nodes, negative cycles, duplicate edges
  • Path reconstruction using predecessor array

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