最近Amazon的面试在技术圈里掀起了不小的波澜。今天我们带来了2道来自Amazon的真实面试题，涵盖了从动态规划、优先队列到面向对象设计的多个领域。这些题目不仅考察了候选人对算法的掌握程度，还检验了他们的代码设计与优化能力。如果你正在为Amazon的面试做准备，那么这些题目将是你不可错过的练习素材！
Recently, Amazon's technical interviews have garnered significant attention in the tech community. Today, we present three real interview questions from Amazon that cover a wide range of topics, including dynamic programming, priority queues, and object-oriented design. These questions not only test a candidate's understanding of algorithms but also assess their ability to design and optimize code effectively. If you're preparing for an Amazon interview, these challenges are essential practice material to sharpen your problem-solving skills!
面试一：最大化在Walmart购买并在Amazon出售的利润
Question:
Sellers buy items in Walmart during sale events and sell them on Amazon to make money. You have to help a seller make maximum profit for their purchase.
You are given two lists, one for Walmart prices and another for Amazon prices of a certain item over a period of time. The ith entry in both lists represents the price of the item at the ith time in Amazon and Walmart.
Example:
Walmart: [4, 7, 5, 8]
Amazon: [6, 8, 7, 8]
Answer: 4; Seller can buy the item at 0th time index from Walmart at $4 and sell it at Amazon on the 1st time index at $8, making the profit of $4.
面试场景还原:
自我介绍:
在面试的开场环节，面试官和候选人进行了短暂的自我介绍。面试官询问了候选人过去的工作经验、参与的项目，以及如何运用算法解决实际问题。候选人解释了自己在最近的工作中如何优化系统性能，并提到了对数据结构和算法的掌握。
面试官：接下来，我们会进入到技术问题的部分。你准备好了吗？
候选人：是的，我准备好了。
题目介绍:
面试官开始介绍问题，描述了在Walmart购买并在Amazon出售的利润最大化问题。候选人很快理解了题目的要求，并提出了一些澄清问题。
澄清问题:
候选人：这道题的意思是我们要寻找在Walmart购买并在Amazon出售的最大利润，对吗？
面试官：是的，关键在于找到适当的购买和出售时间点来获得最高的利润。
候选人：好的，Walmart的价格和Amazon的价格是同时出现的吗？
面试官：是的，两个列表的长度相同，每个时间点上的价格对应相同的商品。
候选人：那么如果Walmart的价格始终高于Amazon的价格，我们可以假设卖家将不会进行交易吗？
面试官：对，卖家可以选择不进行交易，最小的利润为0。
解决思路:
候选人开始描述自己的解决方案，采用了一种类似于动态规划的方法来追踪最低的Walmart价格和最高的Amazon售价。
候选人：我打算使用一个变量来追踪到目前为止看到的最低Walmart价格。然后，我们会遍历Amazon的价格，计算如果以当前最低的Walmart价格购买并在当前的Amazon价格出售的利润。每当我们找到更高的利润时，更新最大利润值。最终结果是最大利润。
面试官：这听起来不错，那么复杂度是多少呢？
候选人：因为我们只遍历一次价格列表，所以时间复杂度是O(n)，其中n是价格的长度。
面试官：好的，那就继续吧。
讨论与追问:
面试官：如果Walmart的价格始终高于Amazon的价格呢？
候选人：在这种情况下，卖家将不会买卖商品，所以利润为0。
面试官：是的，这种边界条件是合理的。你能考虑到哪些其他的边界条件呢？
候选人：例如，如果两个列表的长度不相等，或者价格列表为空，我们可以分别处理这些特殊情况。对于这种问题，我们需要确保输入有效。
面试官：很好，你的解答很清晰。
Clarification and Approach:
The candidate first asked the interviewer: "Is it safe to assume that sellers are allowed to wait for better prices on Amazon after purchasing from Walmart?"
The interviewer confirmed: "Yes, they can wait for a better price at a later time on Amazon after buying the item from Walmart."
Algorithm Discussion:
The candidate proposed an efficient way to approach this problem. The main idea is to track the minimum Walmart price seen so far and calculate potential profits based on subsequent Amazon prices. The candidate discussed initializing variables to track the minimum Walmart price and the maximum achievable profit. For each Amazon price, the profit would be calculated and updated accordingly. At the end, the maximum profit achieved would be returned.
The interviewer approved the approach and asked the candidate to proceed with implementation.
Follow-up Questions:
Interviewer: "What happens if the Walmart price is always higher than Amazon's price? Is there still an opportunity to make a profit?"
Candidate: "If Walmart prices are consistently higher, the seller will choose not to buy, and thus the minimum profit will be 0."
The candidate's approach ensured that no unnecessary purchases were made, and the problem was handled efficiently in O(n) time.
面试二：合并多个有序列表
Question:
Imagine you're interacting with Alexa. When you command Alexa to "acquire a certain item", it begins to retrieve information from a variety of sources, which we'll refer to as 'catalogues'. Each catalogue responds with a sorted list of potential matches. Your task is to write a program that can consolidate these sorted lists into one coherent list.
Example:
- Prime: [2, 5, 8]
- Whole Foods: [1, 3, 4]
- Pantry: [7, 9]
Answer: [1, 2, 3, 4, 5, 7, 8, 9]
面试场景还原:
题目介绍与澄清问题:
面试官提出了一个整合多个有序列表的题目，并询问候选人是否理解题目要求。候选人提问并确认了是否可以使用优先队列（min-heap）来优化列表的合并。
候选人：我们要合并多个有序列表并保持排序，对吗？
面试官：是的，合并后的列表必须保持排序。
候选人：那么我是否可以使用优先队列来加速这一过程呢？因为使用min-heap可以以O(N log k)的时间复杂度完成合并，其中N是元素总数，k是列表的数量。
面试官：没错，这是一个合理的优化方法。
解决思路:
候选人提出了使用min-heap的思路：
- 初始化一个优先队列，将每个有序列表的第一个元素放入队列中。
- 迭代处理，依次将队列中最小的元素放入结果列表中。
- 每当从队列中取出一个元素后，如果该元素所在列表中还有其他元素，将其下一个元素放入队列中。
- 当所有元素都处理完毕时，结果列表即为合并后的有序列表。
讨论与追问:
面试官：如果某些列表长度很大，其他列表长度很小呢？
候选人：min-heap的时间复杂度只与总的元素数目有关，和单个列表的大小关系不大。所以即便列表长度差异较大，这种方法也能很好地处理。
面试官：你还考虑到了哪些可能的边界情况呢？
候选人：如果某些列表为空，或者全部列表为空，我们需要提前处理这些边界条件，以防止不必要的错误。
面试官：很好，代码和思路都很清晰。
Clarification and Approach:
The candidate inquired: "Are we merging multiple sorted lists into one?"
The interviewer responded: "Yes, we are merging several sorted lists, ensuring that the final result is a single sorted list."
Algorithm Discussion:
The candidate explained two possible approaches. The first, brute force, involved concatenating all the lists and sorting the combined list, resulting in an O(N log N) complexity where N is the total number of elements. The second approach was more optimized: the candidate suggested using a min-heap (priority queue) to efficiently merge the lists.
The process involved initializing a min-heap with the smallest element from each list. Then, the candidate would repeatedly extract the smallest element and push the next element from the corresponding list until all elements were merged. This approach guarantees O(N log k) complexity, where k is the number of lists.
The interviewer was satisfied with this approach, noting the efficiency gains, and asked the candidate to proceed with coding.
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