The BFS part clicked fast but I fumbled the tie-breaking for a bit.
Model the problem as a multi-criteria ranking task: first compute shortest distances from the start movie using BFS, then filter out watched movies, and finally sort the remaining by distance, rating, and ID. Discuss how to handle large graphs efficiently and clarify edge cases like unreachable movies.
Pro tip: Mention that you would use a priority queue (min-heap) to merge the three sorting criteria in one pass, avoiding a full sort of all candidates. This shows you think about efficiency and scalability, which is crucial at Google.
Ask about graph size, whether ratings are per user or global, if watched movies should be excluded, and how to handle unreachable movies. Confirm the tie-breaking rules.
Use BFS from the start movie to compute shortest distances to all reachable movies. If the graph is weighted, use Dijkstra's algorithm instead.
Exclude already-watched movies and any unreachable ones. For the remaining, sort by distance ascending, then rating descending, then movie ID ascending.
Instead of sorting all candidates, use a min-heap of size K to keep the top K movies according to the ranking criteria, reducing time complexity to O(N log K).
Discuss time and space complexity. BFS is O(V+E), heap operations are O(N log K). Consider if the graph is large and if pre-processing or caching is possible.
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Went through caching (pre-compute recommendations for popular starting nodes), hot keys if a blockbuster movie suddenly has everyone querying from it, and stale data when ratings update but cached results don't.
Structure your answer by first categorizing production issues into data, model, infrastructure, and operational concerns, then for each category describe a specific failure mode and your mitigation strategy. Emphasize proactive monitoring, graceful degradation, and iterative improvement to show you think beyond just fixing bugs.
Pro tip: Tie every issue back to business impact (e.g., revenue loss, user trust) and propose measurable SLOs to demonstrate you prioritize what matters at Google scale.
Break down production issues into data-related, model-related, infrastructure-related, and operational categories to ensure comprehensive coverage.
For each category, name 1-2 concrete failure modes (e.g., feature drift, model staleness, latency spikes) that could occur at scale.
Describe how you would detect each issue early using metrics, logs, and alerts, and define SLOs to measure impact.
Explain your strategy to handle each issue, such as fallback models, canary deployments, or automated retraining, ensuring graceful degradation.
Conclude by discussing trade-offs between consistency, availability, and cost, and how you would iterate based on post-mortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.