If you want to get Amazon is not easy to get an offer from Amazon. As one of the world's top tech giants, Amazon's interview standards are notoriously high, not just for your technical skills and problem-solving abilities, but also for your compatibility with their famous leadership guidelines.
Whether you want to enter software engineering, or aspire to become a product manager, asking the right questions can help you figure out the doorway and find the secret to success. The next share, let's talk about those Amazon interview "real questions", give you some exclusive insights and strategies, so that you can go to the interview with confidence, from a crowd of experts to stand out!
Amazon Interview Code 1:E-commerce arbitrage profit maximization
Imagine you are an e-commerce arbitrageur who plans to make a profit by purchasing items at a low price from a major retailer (e.g., Walmart) during the promotional season and then selling them at a higher price on another e-commerce platform (e.g., Amazon). Your task is to determine the best time to buy and sell to maximize profits.
Given two arrays representing the daily prices of the same item at Walmart and Amazon on consecutive days. Your goal is to find a combination of timing of purchases and sales that maximizes profit.
Input:
walmart_prices[]: An array of integers representing the prices of items at Wal-Mart on consecutive days.amazon_prices[]: An array of integers representing the prices of items from Amazon on consecutive days.
Output:An integer that represents the maximum profit that can be realized.
Example:
Input:
walmart_prices = [4, 7, 5, 8]amazon_prices = [6, 8, 7, 8]
Output:4
Explanation:You can buy an item from Walmart for $4 on day 0 and sell it on Amazon for $8 on day 1 for a $4 profit.
Attention:
- You can assume that both arrays are of the same length and that only one transaction can be made per day (i.e., you can only buy or sell an item once).
- If there is no profit to be made, the output should be 0.
Interview process:
In the first place:Interviewer and candidate had a short introduction of themselves:
During the interview process, the interviewer delved into the candidate's past professional experience, especially the details of the key projects he/she was involved in, with the aim of assessing his/her practical problem-solving skills. The candidate shared his recent experience of significantly improving the operational efficiency of a system through technological innovation. He emphasized the use of his deep understanding of data structures and algorithms, and these expertise became the means by which he optimized the performance of the system and achieved performance. Through examples, the candidate not only demonstrated his technical strength, but also his ability to flexibly apply theoretical knowledge to practical scenarios.
interviewer: Sounds great, so we move on to the technical interview?
candidatesOkay, no problem.
Next is a code programming question:
The interviewer posed a code question on how to maximize profitability by strategically choosing to buy items at Walmart and sell them on the Amazon platform. The candidate quickly captured the core points of the problem and understood it. To further ensure a full grasp of the problem, the candidate asked a series of clarifying questions.
Clarify questions:
Candidates continue to ask in-depth questions in order to fully understand the problem scenario and boundary conditions: "This question means we are looking for the maximum profit from buying at Walmart and selling at Amazon, right?" This reaffirms the candidate's accurate understanding of the question's objective."
After an affirmative answer from the interviewer, the candidate further asks:: "Okay, are the price lists the same for both?"
Interviewer's clear response: "Yes, both lists are of the same length and the prices at each point in time correspond to the same items."
The candidate took into account the special circumstances and offered:: "So if Walmart's prices are consistently higher than Amazon's, can we assume that the seller will not make the transaction?"
Affirmative answers from the interviewer: "Yes, buyers can strategically choose whether to trade or not, and without buying and selling, the profit is 0″
CodingIdeas:
Candidates begin to elaborate on their solutions:The idea of dynamic programming is used, but is actually closer to a sliding window or single traversal optimization strategy in this particular problem. The candidate plans to use a variable to dynamically track the lowest price seen at Walmart so far, which is a clever strategy because it allows for a quick calculation of the potential profit based on the current lowest Walmart price while traversing the Amazon price.
The interviewer recognized the candidate's thought process: "The idea is fine, so what is the time complexity of such a design?"
The candidate accurately stated: "Since the price list is traversed only once, the time complexity is O(n), where n is the length of the price list."
The interviewer's acknowledged the candidate's answers and asked how to deal with boundary cases, how to ensure the correctness of the code, and how to validate and optimize the algorithmic time complexity in real-world applications.
Discussion of details:
The interviewer then asked:: "Assuming Walmart's prices are consistently higher than Amazon's, how do you pick the right strategy?"
candidates:: "In this case, no transaction occurs for the buyer and the profit is zero."
interviewer:: "So can other boundary conditions be taken into account?"
Candidates respond quickly: "If the lengths of the two lists are not equal or if the price list is empty. For these problems, one needs to make sure that the inputs are valid, which is very important in real-world programming, as illegitimate inputs often lead to program errors or unpredictable results."
interviewer: "Nice, no problem with the solution idea. Then please start implementing the code"
Clarification Question.
Candidates. "So can Walmart Sellers assume that they can buy from Walmart first and then wait for Amazon to provide better pricing?"
The interviewer followed up with "Yes, So the customer can wait and get a price that is lower Amazon in future date after purchasing an item from Walmart."
Code Question Discussion.
The candidate stressed the need to resolve it efficiently. She stressed the necessity to track all Walmart prices seen so far and calculate their profit at She stressed the necessity to track all Walmart prices seen so far and calculate their profit at each step, before calculating what Amazon would make from each price change. She further detailed declaring initial variables such as minimum price from Walmart or maximum profit and updating profit calculations at every Amazon price step until reaching maximum profits so far.
Interviewer liked the proposed technique and advised candidate to implement it immediately.
Follow-up Questions.
The interviewer asked. "Assuming Walmart's prices are always higher than Amazon's, how we choose a suitable strategy to buy goods? "
Candidate. "In this case, we will not make any transactions and the profit will be zero."
Interviewer. "Let's consider other boundary conditions."
The candidate responded. "If the lengths of two lists are not equal or the price list is empty, it is important to ensure that the input is valid for these issues, as "If the lengths of two lists are not equal or the price list is empty, it is important to ensure that the input is valid for these issues, as invalid input often leads to program errors or unpredictable results.
Interviewer. "Great, there is no problem with the solution idea. Then please start implementing the code."
Amazon Interview Code 2: Merging N Ordered Lists
Question:
Example:
Interview process.
Code Title Quiz
Candidates ask in-depth questions: The core of the task, the need to merge multiple sorted lists into a single new list that also remains ordered, is further clarified. The solution is proposed immediately after: "When merging these lists, is it possible to optimize the process with the help of a priority queue (in particular, a min-heap)? By constructing a min-heap to dynamically track the current minimum element of each list, we are able to achieve a more efficient merging algorithm with a time complexity of approximately O(N log k), where N represents the total number of elements in all lists and k is the number of lists. Could such an approach be applicable to our scenario?"
The interviewer gave positive feedback:"Your thinking is very clear and correct. Using a minimal heap to assist in merging multiple ordered lists is indeed an effective optimization tool. This approach not only maintains the orderliness of the merged lists, but also significantly improves the efficiency of the algorithm by reducing the number of unnecessary comparisons."
Code ideas:
The idea proposed by the candidate of using a min-heap (minimal heap) to merge multiple ordered lists is very clear and effective. Below are the detailed steps of the idea, along with an explanation of each step:
- Initialize priority queue (min-heap): Create a minimal heap to store the current minimal element from each ordered list.
- Iterative processing of the minimal heap: In a loop, the current smallest element (i.e., the top element of the heap) is continually removed from the minimal heap and added to the result list.
- Consolidated results: When the minimal heap is empty, it means that all the elements of the list have been processed.
Clarification Question.
The candidate asked:: "Are we combining multiple sorted lists into one?
The interviewer replied, "Yes, while merging multiple sorted lists, the resultant has to be a single sorted array".
Code Discussion.
The product guy elaborated upon two alternatives (predictions). One is the brute force method of simply concatenating every list together and then sorting that resulting collection which takes O(N log N) time, where N represents the total number of elements. The second approach made more sense: Candidate had advised using a min-heap (priority queue) to easily merge the lists in an optimized way.
It requires the initialisation of a min heap with minimum element from each list. Such candidate would keep extracting smallest element and pushing the next successor from that list until all elements were merged. This one gives you O(N log k) complexity, with respect to the number of lists which is denoted here as k.
The interviewer was pleased with that answer, recognizing the potential time savings, and asked for coding to begin.
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