Nailed the BFS part first pass, no issues.
Start by clearly identifying the multiple sources in the 1D array and explain why a simultaneous BFS from all sources is more efficient than running BFS from each source independently. Walk through the naive solution first, then systematically optimize by leveraging the multi-source BFS pattern to achieve O(n) time complexity. Make sure to verbalize your thought process and trade-offs at each stage.
Pro tip: Google interviewers value candidates who proactively discuss the 'why' behind algorithmic choices — explicitly state that multi-source BFS avoids redundant work by treating all sources as a single virtual node at distance 0, which is a hallmark of senior-level thinking. Also mention edge cases like an empty array or all elements being sources before diving into code.
Ask clarifying questions to confirm what constitutes a 'source' in the array, what the BFS is measuring (e.g., minimum distance to nearest source), and any constraints on array size or values. Restate the problem in your own words to confirm alignment with the interviewer.
Propose the brute-force solution of running a separate BFS or linear scan from each source individually, and analyze its time complexity — typically O(n * k) where k is the number of sources. Acknowledge this is suboptimal to set up the optimization discussion.
Explain the key insight: initialize the BFS queue with all source nodes simultaneously at distance 0, treating them as if they originate from a single virtual super-source. Walk through how the BFS frontier expands outward from all sources in parallel, guaranteeing each cell is visited only once.
Write clean, well-commented code for the multi-source BFS, using a deque/queue and a distance array initialized to infinity (or -1). Trace through a concrete example (e.g., [0, 1, 0, 0, 1] finding distances to nearest 0) to validate correctness and demonstrate clarity.
State the final time complexity of O(n) and space complexity of O(n) for the distance array and queue. Mention potential further optimizations such as a two-pass linear scan (left-to-right then right-to-left) that achieves the same result with O(1) extra space, demonstrating depth of knowledge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spent like 20 minutes just trying to figure out what was being asked.
Treat the ambiguity as part of the problem: systematically ask clarifying questions to pin down inputs, outputs, constraints, and edge cases before proposing an algorithm. Restate the problem in your own words to confirm understanding, then walk through a concrete example and outline a solution with complexity analysis.
Pro tip: Don't rush to code; interviewers at Google evaluate how you navigate ambiguity. Verbally prioritize which clarifications matter most and explain why, showing structured thinking under uncertainty.
Ask targeted questions about input format, output expectations, constraints, and edge cases. Confirm your understanding by restating the problem in your own words.
Explicitly state any assumptions you're making and ask if they're valid. Discuss potential ambiguities and how they affect the solution.
Choose a simple but representative example to illustrate the problem and your intended approach. Use it to verify your understanding with the interviewer.
Outline a high-level approach, then dive into details. Discuss data structures, time and space complexity, and possible optimizations.
Walk through edge cases and test your solution against them. If needed, refine the algorithm based on feedback or new insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.