Software Engineering Intern - Summer 2025

Meta

Write code to parse regular expressions including * (0 or more characters), + (1 or more characters), and . (1 exact character). For example: parse(a., ab) should return true. parse(a*, aaa) should return true. parse(a+, a) should return true. parse(a+, ) should return false. parse(a.*b, axyzb) should return true. parse(a.*b, axyz) should return false. parse(abc, abc) should return true. parse(abc, abd) should return false. Your function should take two strings as input: the regular expression and the string to match against. It should return a boolean indicating whether the string matches the regular expression. Focus on handling the special characters ., *, and +. Assume that other characters in the regular expression must match literally. Explain your approach and provide a clear, well-documented code implementation. Consider edge cases such as empty strings and null inputs. Also think about the time and space complexity of your solution, and try to optimize it for performance. Can you provide a solution using dynamic programming?

Meta

Design a high data volume real-time data pipeline, discussing both high-level and low-level details. For example, imagine you're building a real-time clickstream processing pipeline for an e-commerce website that receives millions of clicks per minute. The pipeline needs to process this data to provide real-time insights such as top-selling products, website traffic analysis, and fraud detection. Consider aspects such as data ingestion, storage, processing, and serving. Discuss the technologies you would choose for each stage and justify your choices. Also, address potential challenges like data volume, velocity, variety, and veracity. How would you ensure the pipeline is scalable, reliable, and fault-tolerant? What monitoring and alerting mechanisms would you implement? How would you handle backpressure and ensure data consistency? Finally, how would you optimize the pipeline for cost-effectiveness and performance?

Meta

Could you describe a situation where you faced a significant memory leak issue in a C++ application and how you diagnosed and resolved it? Please walk me through the tools and techniques you used, the challenges you encountered, and the ultimate solution you implemented. For example, imagine you're working on a large-scale C++ application for processing high-volume financial transactions. The application is experiencing performance degradation over time, eventually leading to crashes. After initial investigation, you suspect a memory leak. Describe your approach to identifying the source of the leak. Consider discussing the specific tools you might employ, such as memory profilers (e.g., Valgrind, AddressSanitizer), debugging techniques (e.g., heap analysis), and code review strategies. What were some of the initial hypotheses you formed, and how did you validate or refute them? What were the key code areas you focused on based on your initial findings? Furthermore, explain any specific coding practices or patterns that contributed to the leak (e.g., improper use of smart pointers, forgetting to release dynamically allocated memory). How did you correct these issues, and what steps did you take to prevent similar leaks from occurring in the future? Did you implement any automated testing or monitoring to detect memory leaks early on? Finally, reflect on any lessons learned from this experience and how it has shaped your approach to memory management in C++.