Analog/Mixed Signal Product Engineer

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

System Design Question: Designing a URL Shortening Service like TinyURL Let's design a URL shortening service like TinyURL. Consider the following: Functional Requirements: Given a long URL, the service should generate a shorter and unique alias. When users access the short URL, they should be redirected to the original long URL. The system should be highly available and reliable. Analytics: Track the number of times a short URL has been accessed. Non-Functional Requirements: The system should have low latency for URL redirection. Shortened URLs should be relatively short in length. The system should be able to handle a large number of URL shortening and redirection requests. Scalability: The system should be scalable to handle a growing number of URLs and users. Capacity Estimation & Constraints: Assume 100 million URLs are shortened per day. Assume 1 billion redirections per day. Assume a read/write ratio of 10:1 (redirections vs. shortening). Short URL length should be as short as possible (e.g., 7 characters). High-Level Design: Discuss the components involved (e.g., web servers, application servers, database). Explain the URL shortening and redirection process. Propose a suitable database (e.g., SQL or NoSQL). Detailed Design: How to generate unique short URLs? Discuss approaches like hash functions, base-62 encoding, or UUIDs. What are the tradeoffs of each approach in terms of collision probability, URL length, and performance? Explain the database schema for storing the mappings between short and long URLs. Detail the caching strategy to improve redirection performance. Where should the cache be placed (e.g., client-side, CDN, in-memory cache)? What cache eviction policy should be used? Scaling and Availability: How to scale the system to handle the expected load? Discuss techniques like load balancing, database sharding, and replication. How to ensure high availability? Discuss strategies like redundancy, failover mechanisms, and monitoring. Rate Limiting: Implement rate limiting to prevent abuse (e.g., a user shortening too many URLs in a short period of time). Discuss different rate-limiting algorithms (e.g., token bucket, leaky bucket). Walk through the design process, addressing each of these points. Explain any tradeoffs you make in your design decisions. Show examples of how different technologies or architectural choices impact system performance and scalability.

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?