AIML - Machine Learning Engineer, Data and ML Innovation

Apple

Let's explore dynamic programming with a classic problem: the knapsack problem. Imagine you have a knapsack with a limited weight capacity, and a collection of items, each with its own weight and value. Your goal is to select the items to put into the knapsack to maximize the total value, without exceeding the weight capacity. Specifically: Problem Statement: You are given a knapsack with a maximum weight capacity W. You are also given n items. Each item i has a weight wi and a value vi. Determine the maximum total value of items that can be placed in the knapsack without exceeding its weight capacity. Example: Knapsack capacity W = 10 Items: Item 1: weight = 6, value = 30 Item 2: weight = 3, value = 14 Item 3: weight = 4, value = 16 Item 4: weight = 2, value = 9 What is the maximum value you can obtain by putting items into the knapsack, and which items should you choose? Explain how you would approach this problem using dynamic programming. Be sure to define the subproblems, the recurrence relation, and the base cases. Also, discuss the time and space complexity of your dynamic programming solution.

Apple

Let's design a system for URL shortening, like TinyURL. Assume a large-scale service. Please consider the following aspects in your design: Functional Requirements: The system should generate shorter URLs (a.k.a. short links) given longer URLs. Users should be able to navigate to the original URL by entering the short link in their browser. The system should be highly available. Non-Functional Requirements: The system should have low latency for both URL shortening and redirection. Short links should be relatively short (e.g., 6-8 characters). The system should be able to handle a large number of URL shortening and redirection requests. Capacity Estimation: Assume 100 million URLs are shortened per day. Assume 1 billion URL redirections per day. Estimate storage requirements for 5 years. Design Considerations: How would you generate unique short links? What data structures and databases would you use to store the URL mappings? How would you handle collisions when generating short links? How would you optimize for low latency redirection? How would you scale the system to handle the expected traffic? Walk me through your design, explaining your choices and the trade-offs you considered. Provide diagrams where necessary to illustrate the architecture.

Apple

Let's explore your resume and then dive into a coding question. First, regarding your resume: Could you walk me through a project that you are particularly proud of? Describe your role, the challenges you faced, and the solutions you implemented. Be specific about the technologies you used and the impact your contributions had on the project's outcome. Tell me about a time you had to work with a technology or framework that you were unfamiliar with. How did you approach learning it, and how did you apply it to the project at hand? What resources did you find most helpful, and what challenges did you encounter during the learning process? Describe a situation where you had to make a trade-off between different approaches or technologies. What factors did you consider when making your decision, and how did you evaluate the potential consequences of each option? Can you provide a specific example of such a trade-off? Now, for the coding question: Given a list of integers, write a function to find the contiguous sub-array with the largest sum. For example, if the input is [-2, 1, -3, 4, -1, 2, 1, -5, 4], the output should be [4, -1, 2, 1], with a sum of 6. Explain your approach, and provide a working implementation of the function in your preferred programming language. Consider edge cases such as an empty input list or a list with only negative numbers. What is the time complexity of your solution? Can you optimize it further?