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DRW·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate
Jun 2026

Summary

Three algorithmic problems for a DRW software engineer role. The problems escalated nicely in difficulty and felt more like a competitive programming set than a typical interview. No behavioral stuff, just pure problem solving.

Questions Asked (3)

Q1

Given an integer N (up to 200,000), construct any string of lowercase letters with length N such that every letter appearing in the string appears an odd number of times.

Algorithms & Data Structures
Author's notes

This one's more of a warm-up.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and edge cases (e.g., N=1). Then, derive a simple construction: if N is odd, use all 'a's; if N is even, use one 'b' and N-1 'a's. Verify that every letter appears an odd number of times.

Pro tip: Mention that the construction is O(N) time and O(1) extra space, and that it handles all N up to 200,000 efficiently. Also, note that the problem allows any string, so simplicity is key.

1. Understand the problem

Restate the problem: given N, output any string of length N where each distinct character occurs an odd number of times. Consider edge cases like N=1.

2. Identify parity constraints

The sum of odd counts must equal N. If N is odd, one letter with odd count works. If N is even, an even number of letters with odd counts is needed, so at least two letters.

3. Construct a simple solution

For odd N, use all 'a's. For even N, use one 'b' and N-1 'a's. This ensures 'a' appears odd (N-1 is odd) and 'b' appears odd (1).

4. Verify and analyze

Check that the string length is N and each character count is odd. Analyze time and space complexity: O(N) time to build the string, O(1) extra space.

Key Points to Mention

  • Parity of N determines the number of distinct letters needed.
  • Simple construction using at most two distinct letters.
  • Time complexity O(N) and space complexity O(1) extra.
  • Edge case N=1: string 'a' works.
  • The solution is optimal in terms of simplicity and efficiency.
  • No need for complex data structures; direct string construction suffices.

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

Q2

Given two digit strings S and T of equal length, you can swap S[i] with T[i] at any subset of indices. Among all subsets that minimize the absolute difference between the two resulting integers, find the one that uses the fewest swaps. Return that minimum swap count.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This was the hardest one for me.

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AI HintsAI Generated

Suggested Approach

First, clarify that the goal is to minimize the absolute difference between the two resulting integers, and among all subsets achieving that minimum difference, find the one with the fewest swaps. Then, propose a dynamic programming solution that processes digits from most significant to least, tracking the difference and swap count, or a greedy approach if the structure allows. Finally, discuss time and space complexity and potential optimizations.

Pro tip: Emphasize that minimizing the integer difference is equivalent to minimizing the absolute difference of the digit strings interpreted as numbers, and that lexicographic comparison from the most significant digit is key. Also, mention that you would test edge cases like leading zeros and equal strings.

1. Understand the problem and constraints

Restate the problem: given two equal-length digit strings S and T, you can swap characters at any subset of indices. Find the minimum number of swaps needed to achieve the smallest possible absolute difference between the two resulting integers.

2. Identify the core challenge

The main challenge is that minimizing the difference and minimizing swaps are two objectives. You need to first find the minimal achievable difference, then among all swap subsets that achieve it, find the one with the fewest swaps.

3. Propose an algorithm

Consider a dynamic programming approach that processes digits from left to right, maintaining the current difference and the number of swaps. Alternatively, if the minimal difference is 0 or 1, a greedy strategy might work. Explain how to handle the two objectives.

4. Analyze complexity and trade-offs

Discuss the time and space complexity of your approach. For DP, it might be O(n * D) where D is the range of possible differences. Mention any optimizations or alternative approaches.

5. Test with examples and edge cases

Walk through a simple example, such as S='12', T='21', to show how the algorithm works. Also consider edge cases like leading zeros, identical strings, and strings with all same digits.

Key Points to Mention

  • The difference between the two integers is determined by the most significant digit where they differ.
  • Swapping at an index changes both numbers: S[i] and T[i] are exchanged.
  • The minimal possible difference is often 0 (if strings can be made equal) or 1 (if not).
  • Dynamic programming can track the minimal difference and minimal swaps simultaneously.
  • Greedy approaches may work for special cases but need careful justification.
  • Time complexity should be at most O(n^2) or better, given typical constraints.

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

Q3

Given N patients each with exactly two possible slot assignments (A[k] and B[k]), and S total slots where each slot can hold at most one patient, determine whether a valid assignment exists.

Algorithms & Data StructuresSystem Design
Author's notes

Classic 2-SAT or bipartite matching problem dressed up in a scheduling scenario.

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AI HintsAI Generated

Suggested Approach

Model the problem as a bipartite matching between patients and slots, where each patient has edges to exactly two slots. Since each patient has degree 2, the problem reduces to checking if a perfect matching exists in a graph where each connected component is either a path or a cycle; use union-find to detect cycles and count edges vs vertices in each component.

Pro tip: Mention that this is a special case of 2-SAT or bipartite matching, but the degree-2 property allows an O(N α(N)) union-find solution, which is optimal and elegant. Also, clarify that if any component has more edges than vertices, no assignment exists.

1. Model as a graph

Create a graph where each patient is a node and each slot is a node, with edges connecting each patient to their two possible slots. The goal is to find a matching that covers all patients.

2. Identify components

Use union-find to group connected components. Each component is a set of patients and slots connected by edges.

3. Check feasibility per component

For each component, count the number of patients (P) and slots (S). A valid assignment exists if and only if for every component, P ≤ S. Since each patient has degree 2, this condition simplifies to checking if the number of edges (2P) is at most the number of vertices (P+S), i.e., P ≤ S.

4. Handle cycles and paths

If a component is a cycle (P = S), it can be perfectly matched. If it's a path (P < S), it can also be matched. If P > S, impossible.

5. Return result

If all components satisfy P ≤ S, return true; otherwise, return false.

Key Points to Mention

  • Bipartite matching formulation
  • Degree-2 property of patients
  • Union-Find (Disjoint Set Union) for component detection
  • Condition P ≤ S per component
  • Time complexity O(N α(N)) or O(N) with path compression
  • Alternative: 2-SAT or max-flow, but union-find is optimal

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