Software Engineer III, Google Cloud Application Modernization

Google

Tic-tac-toe is played by two players A and B on a 3 x 3 grid. The rules of Tic-Tac-Toe are: Players take turns placing characters into empty squares ''. The first player A always places 'X' characters, while the second player B always places 'O' characters. 'X' and 'O' characters are always placed into empty squares, never on filled ones. The game ends when there are three of the same (non-empty) character filling any row, column, or diagonal. The game also ends if all squares are non-empty. No more moves can be played if the game is over. Given a 2D integer array moves where moves[i] = [rowi, coli] indicates that the ith move will be played on gridrowi. return the winner of the game if it exists (A or B). In case the game ends in a draw return Draw. If there are still movements to play return Pending. You can assume that moves is valid (i.e., it follows the rules of Tic-Tac-Toe), the grid is initially empty, and A will play first. For example: moves = [[0,0],[2,0],[1,1],[2,1],[2,2]] Output: A moves = [[0,0],[1,1],[0,1],[0,2],[1,0],[2,0]] Output: B moves = [[0,0],[1,1],[2,0],[1,0],[1,2],[2,1],[0,1],[0,2],[2,2]] Output: Draw

Google

Let's design a rate limiter for an API. The rate limiter should: Limit the number of requests a user can make to an API within a given time window. Support different rate limits for different users or API endpoints. For example, a free tier user might be limited to 10 requests per minute, while a premium user can make 100 requests per minute. Similarly, some API endpoints might have lower rate limits than others due to resource constraints. Be highly performant to avoid adding significant latency to API requests. Be scalable to handle a large number of users and API requests. Be reliable and prevent accidental overloads that could cause service disruptions. Describe the architecture, algorithms, and data structures you would use to implement such a rate limiter. Consider the following: How would you store the rate limit counters for each user or endpoint? What algorithm would you use to determine whether a request should be allowed or throttled? How would you handle distributed rate limiting across multiple servers? How would you ensure the rate limiter is resilient to failures? As an example, imagine a scenario where a social media platform wants to prevent users from spamming their API with excessive requests to post updates. The platform has millions of users and thousands of API endpoints. Some users pay for premium access with higher request limits. How would you design a robust and efficient rate limiter to handle this scale and these varying requirements?

Google

Tell me about a time you had to work with a signed contract. Describe the situation, your role, and the outcome. What were the key clauses or provisions that were most relevant to the situation? What challenges, if any, did you face in interpreting or adhering to the contract terms? How did you ensure that your actions were in compliance with the contract, and what steps did you take to mitigate potential risks or disputes? For example, consider a scenario where you were managing a project with a vendor, and the contract outlined specific deliverables, timelines, and payment terms. A conflict arose when the vendor failed to meet a critical deadline, potentially impacting the project's overall timeline and budget. How did you leverage the contract to address the issue, protect your company's interests, and find a resolution that was fair to both parties? Or, imagine you were involved in a negotiation where the other party wanted to change certain terms after the contract was signed. How did you handle the situation, ensuring that any modifications were properly documented and agreed upon by all parties involved? What did you learn from this experience, and how has it influenced your approach to working with contracts in subsequent projects or situations?