Focuses on general problem solving strategies and structured thinking applicable to engineering, coding, and complex decision making. Core skills include clarifying the problem, breaking problems into subproblems, identifying patterns, selecting appropriate approaches and data structures, developing and testing incremental solutions, analyzing trade offs, reasoning about time and space complexity, handling edge cases, and communicating thought process clearly. Includes algorithmic patterns and design of systematic approaches to unfamiliar problems as well as frameworks for organizing thought under ambiguity.
MediumTechnical
99 practiced
Implement in Python BFS shortest_path(adj: Dict[int, List[int]], start: int, goal: int) -> List[int] that returns a shortest path between start and goal in an unweighted graph. The graph may be large; implement iteratively (no recursion) and explain memory considerations and how to handle a graph that is partially streamed from disk.
HardTechnical
59 practiced
A stakeholder asks you to retrain to improve fairness across demographic groups, but some under-represented groups have very limited labeled data. Propose a systematic approach including data collection strategies, targeted labeling, synthetic data or augmentation options, reweighting and constrained optimization methods (e.g., equality of opportunity, group-aware loss), evaluation metrics for fairness, and how to communicate trade-offs and uncertainty to stakeholders.
HardTechnical
98 practiced
Model-serving latency occasionally spikes beyond the SLO. Propose a structured troubleshooting plan: list the metrics to collect (CPU/GPU utilization, GC pause times, thread pool saturation, queue lengths, I/O wait), immediate mitigations (circuit breaker, shedding traffic, warm pools), and longer-term fixes (resource isolation, tuning batching, improving cold-start time). Explain how you'd reproduce and validate the root cause.
HardTechnical
63 practiced
Describe and sketch an implementation in Python (or pseudocode) for external merge sort to sort a file containing 10 billion 64-bit integers that do not fit in memory. Discuss chunk size selection based on RAM, how to write and manage sorted runs, a k-way merge strategy, I/O buffering, use of temporary files, parallelization strategies, and validation checks to ensure correctness and performance.
MediumTechnical
111 practiced
Explain differences between reservoir sampling, random shuffle followed by truncation, and sampling with replacement. For ML pipelines where you need a uniform random sample of size k from N records on disk or stream, compare time, space, and practical constraints and state when you'd prefer each approach.
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