Salesforce·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Salesforce software engineer interview with a dynamic programming problem that looks straightforward until you actually sit down and think about it. The problem was well-defined but the optimal approach took me longer to see than I'd like to admit.

Questions Asked (1)

Given an array of campaign costs and a number of weeks, partition the array into exactly that many contiguous non-empty groups. Each week's cost is the max of its group. Return the minimum possible sum of those per-week maxima.

Algorithms & Data Structures

Author's notes

My first instinct was greedy and it was wrong.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem and constraints, then propose a dynamic programming solution where dp[i][j] represents the minimum sum for partitioning the first i elements into j groups. Optimize the transition using a monotonic stack or divide-and-conquer optimization to achieve O(n*k) or O(n log n) time, and analyze time/space complexity.

Pro tip: Mention that the cost function (max of subarray) satisfies the quadrangle inequality, enabling divide-and-conquer optimization to reduce time complexity from O(k*n^2) to O(k*n log n). This shows deep algorithmic insight and can impress the interviewer.

1. Clarify the problem

Restate the problem to ensure understanding: partition array into exactly k contiguous non-empty groups, minimize sum of group maxima. Ask about constraints (n, k, value ranges) and edge cases (k > n, k = 1, k = n).

2. Define DP state and recurrence

Define dp[i][j] as the minimum sum for partitioning the first i elements into j groups. Recurrence: dp[i][j] = min_{p < i} (dp[p][j-1] + max(arr[p+1..i])). Base cases: dp[0][0] = 0, dp[i][0] = infinity for i > 0.

3. Optimize the transition

Naive transition is O(n^2 * k). Optimize using monotonic stack to maintain candidate maxima and a segment tree or divide-and-conquer optimization to reduce to O(n*k) or O(n*k log n). Explain the optimization clearly.

4. Analyze complexity and edge cases

State time and space complexity of the optimized solution. Discuss edge cases: k = 1 (answer is max of entire array), k = n (answer is sum of all elements), and when k > n (impossible, return -1 or handle as per problem).

5. Test with examples

Walk through a small example (e.g., arr = [1,2,3,4], k=2) to verify the DP and optimization. Mention potential pitfalls like integer overflow and off-by-one errors.

Key Points to Mention

Dynamic programming with state dp[i][j] and transition using max of subarray.
Monotonic stack to efficiently compute max for all subarrays ending at i.
Divide-and-conquer optimization or Knuth optimization due to quadrangle inequality.
Time complexity: O(k * n log n) or O(k * n) with advanced optimization; space O(n*k) or O(n) with rolling array.
Edge cases: k=1, k=n, k>n, and large input sizes requiring efficient solution.
Trade-offs between different optimization techniques and their applicability.

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