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Handling Problem Variations and Constraints Questions

This topic covers the ability to adapt an initial solution when interviewers introduce follow up questions, new constraints, alternative optimization goals, or larger input sizes. Candidates should quickly clarify the changed requirement, analyze how it affects correctness and complexity, and propose concrete modifications such as changing algorithms, selecting different data structures, adding caching, introducing parallelism, or using approximation and heuristics. They should articulate trade offs between time complexity, space usage, simplicity, and robustness, discuss edge case handling and testing strategies for the modified solution, and describe incremental steps and fallbacks if the primary approach becomes infeasible. Interviewers use this to assess adaptability, problem solving under evolving requirements, and clear explanation of design decisions.

EasyTechnical
0 practiced
Explain how external (external-memory) sort works for files larger than available RAM. Provide the step-by-step algorithm and mention where bottlenecks appear as input sizes and parallelism increase.
HardTechnical
0 practiced
Describe how you would achieve end-to-end exactly-once delivery semantics for a pipeline that reads events from Kafka, processes them in Spark Structured Streaming, and writes results to a relational database. Discuss transactional boundaries, idempotence, and whether distributed transactions are feasible.
MediumTechnical
0 practiced
You must backfill 6 months of historical data through a streaming pipeline that normally processes real-time events. Provide a safe backfill plan that avoids duplicate processing, respects current data integrity, ensures progress, and can be throttled. Compare a pure-batch backfill vs streaming backfill and describe fallback steps.
MediumTechnical
0 practiced
Your company wants to migrate storage from JSON files to Parquet to improve analytics performance. Describe the migration plan including schema inference, compression/encoding choice, handling nested fields, validation strategy, and rollback plan if downstream jobs break.
EasyTechnical
0 practiced
Implement a Python function that maintains the top-k largest integers from a stream of numbers, using O(k) memory. Provide the function signature and briefly explain complexity. (You do not need to include unit tests, but explain how you'd test corner cases.)

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