Security Software Engineering Manager - Systems & Kernel

Apple

Given an integer n, return the count of all numbers with unique digits, x, where 0 <= x < 10^n. For example: Input: n = 2 Output: 91 Explanation: The answer should be the total numbers in the range of 0 <= x < 100, excluding 11,22,33,44,55,66,77,88,99 Input: n = 0 Output: 1

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 say a developer doesn't agree that a reported bug is actually a bug. How would you handle this situation? Be specific. For example, imagine you're a QA engineer, and you find that a button on a webpage is slightly misaligned in a specific browser. You log this as a bug, but the front-end developer says it's 'within acceptable tolerances' and refuses to fix it. Or, perhaps a back-end developer claims that a specific edge-case error is so rare it's not worth the effort to address, even though you believe it violates the product's stated reliability goals. What steps would you take to resolve the disagreement while maintaining a positive working relationship and ensuring product quality? Consider the different approaches you could take based on the severity and impact of the potential bug.