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Recently, I interviewed for a software engineering position with Amazon and encountered a scalable system problem involving minimum value. Here I will share my experience, detailing both the interview process and proposed solution(s).

The interviewer presented the following issue:

Problem Statement:

AWS provides scalable systems. A set of n servers are used for horizontally scaling an application. The goal is to have the computation a power of the servers in non-decreasing order. Todo so, you can increase the computational power of each server in any contiguous segment by x.Choose the values of x such that after the computational powers are in non-decreasing order, the sum of the x values is minimum.

Given n = 5 servers with computational powers [3, 4, 1, 6, 2].

Example List: [3, 4, 1, 6, 2]

Output: 7


The interview began with a brief introduction, and then the interviewer moved straight to the coding problem.


I started by writing the main function to note that adding values to substrings is essentially a greedy property of adding values to suffixes:

def min_increments_to_non_decreasing(n, powers):
    total_increments = 0
    for i in range(1, n):
        if powers[i] < powers[i - 1]:
            increment = powers[i - 1] - powers[i]
            powers[i] += increment
            total_increments += increment
    return total_increments

n = 5
powers = [3, 4, 1, 6, 2]
result = min_increments_to_non_decreasing(n, powers)
print("Minimum total increments:", result)

Me: “Here’s the main function. Initialize total_increments to 0.Iterate through the list from the second element to the last.The result will be the total sum of increments needed to make the computational powers non-decreasing.”

Interviewer: “Great. Can you explain the logic of the min_increments_to_non_decreasing function?”

Me: “If the current server’s power is less than the previous server’s power:Calculate the difference, Add the difference to the current server’s power, Add the difference to total_increments.”

Interviewer: “Looks good. Your approach is efficient and handles the recursive checks well. Do you have any thoughts on optimizing it further?”

Me: “One potential optimization could be to use memoization to store results of previously computed, which would avoid redundant calculations and speed up the process.”

Interviewer: “Excellent point. Memoization could indeed improve the performance. Another thing to consider is handling edge cases.”

Me: “Absolutely. Handling edge cases is crucial to ensure robustness. I would also consider using dynamic programming to store intermediate results and avoid redundant computations.”


Interview Day came to a close with a brief discussion on possible optimizations and edge cases, providing me with insight into Amazon’s problem-solving style and expectations. Overall, it was an eye-opening experience which gave me greater confidence to tackle similar problems in future interviews.

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Through our powerful Amazon Interview Proxy, the candidate successfully navigated these interview questions. The analysis and discussion not only showcased the candidate’s programming skills but also demonstrated their clear problem-solving approach and effective communication abilities. These insights are valuable not only for tackling amazon’s interviews but also for enhancing our capability to solve real-world programming challenges. Good luck to everyone with your interviews!



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