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.
EasyTechnical
72 practiced
Implement in Python a function merge_two_sorted_arrays(a: List[int], b: List[int]) -> List[int] that merges two sorted arrays into a single sorted array in O(n+m) time and O(1) additional space (aside from output). Provide your implementation and show the result for a=[1,3,5], b=[2,4,6]. Discuss how you would adapt the approach if arrays were stored as files on disk.
HardTechnical
57 practiced
Case study: Analysts report that daily aggregated sales numbers sometimes disagree with hourly roll-ups summed over the day. Design a diagnostic plan to find the cause: include checks for timezone differences, partition boundaries, late-arriving events, duplicate processing, rounding/decimal issues, and aggregation windows. Propose a fix for the most common root causes and explain how to validate and prevent recurrence.
HardSystem Design
107 practiced
Design an end-to-end streaming ETL that provides exactly-once semantics to the analytical sink (e.g., a data warehouse) using Kafka as the ingestion layer and your compute engine of choice. Describe components, how to achieve idempotent or transactional writes, how to handle retries and failures, and trade-offs involved (throughput, complexity, latency).
HardTechnical
72 practiced
A Spark job that flattens nested JSON and performs a large join is running out-of-memory and suffering long GC pauses. Provide a prioritized, step-by-step optimization plan you would execute: consider pruning fields, using parquet projection, increasing parallelism, changing join strategy (broadcast vs shuffle), adjusting memory tuning (executor memory, off-heap), and mitigating skew. Explain how you would measure success at each step.
MediumTechnical
78 practiced
Implement in Python a function sliding_window_max(arr: List[int], k: int) -> List[int] that returns the maximum for every contiguous subarray of size k. Aim for O(n) time using a deque. Provide an example with arr=[2,1,3,4,6,3,8,9,10,12,56] and k=4 and show the expected output.
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