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MEDIUMArray, Binary SearchThis problem looks geometric, but the core idea is much simpler than it first appears.
The task is to place a horizontal line y = cut so that:
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Selected problem: Ad Click Aggregation
Scope: Design a high-throughput ad event aggregation pipeline that ingests impression and click events, deduplicates them, produces near-real-time campaign rollups, and supports slower reconciled reporting for analytics, optimization, and billing-adjacent use cases.
This interview problem is less about serving ads and more about turning a firehose of raw events into trustworthy aggregates. Grokking and Alex Xu push the same early habit here: clarify scope, keep the write path simple and durable, and decouple slow downstream consumers with queues or logs. DDIA adds the deeper constraint: aggregated counters are derived data, so correctness depends on event ordering, idempotence, late-event handling, and replay, not on pretending one synchronous write magically updates every store exactly once.
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MEDIUMArray, Dynamic ProgrammingThe input gives a target amount and a list of coin denominations. Each denomination can be used as many times as needed.
The task is to count how many distinct combinations can make the amount exactly. It is not asking for the minimum number of coins, and it is not asking whether the amount is possible at all.
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Selected problem: Video Streaming Platform
Scope: Design a global video-on-demand platform that supports resumable creator uploads, transcoding, metadata/search, and adaptive-bitrate playback on web, mobile, and TV clients; exclude recommendations, ads, and live streaming from the first version.
This is the classic “design YouTube / Netflix-style VOD platform” interview. Grokking is useful for the interview flow: clarify scope, define APIs early, size uploads versus reads, and separate large media blobs from metadata. Alex Xu adds the practical serving path: object storage for source and encoded media, an asynchronous transcoding pipeline, CDN-based delivery, and a metadata control plane. DDIA sharpens the deeper answer: treat search indexes, counters, and playback-ready state as derived data maintained from durable logs and background workflows, not as one giant transactional database.
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MEDIUMArray, Dynamic Programming, Breadth-First SearchThe input gives a set of coin denominations and a target amount. Each coin can be used as many times as needed.
The goal is not to count how many different ways the amount can be formed. The goal is to find the fewest coins needed to reach the exact amount. If the amount cannot be formed exactly, the answer is -1.
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Selected problem: ChatGPT
Scope: Design a text-first ChatGPT-style assistant that supports multi-turn conversations, streamed responses, document-backed answers, bounded tool use, and durable conversation history for consumers and teams.
This interview problem combines ideas from several classic designs instead of mapping neatly to one old chapter. Grokking’s framework still applies: clarify scope, define APIs, estimate scale, pick a data model, then explain the high-level design and bottlenecks. Alex Xu’s chat-system chapter is useful for session handling, streaming delivery, and multi-device conversation sync; the YouTube chapter is useful for large-object storage and asynchronous processing; DDIA adds the deeper foundation for logs, caches, replication, partitioning, derived data, and backpressure. The key modern shift is that the assistant is not only a chat transport problem. It is a stateful product sitting on top of an expensive inference pipeline.
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HARDArray, Queue, Sliding Window, Heap (Priority Queue), Monotonic QueueThe input gives an array and a window size k. Imagine placing a length-k window over the first k elements, recording the maximum value inside that window, then moving the window one step to the right and repeating until the end.
Generated by Codex with GPT-5
MEDIUMArray, Binary Search, Sliding Window, Prefix SumThis problem sounds like a bit-flipping question, but the useful reframe is simpler:
find the longest contiguous subarray that contains at most k zeroes.
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MEDIUMArray, Dynamic ProgrammingThis problem asks for the contiguous subarray whose product is as large as possible.
That sounds close to Maximum Subarray, but products behave very differently from sums. With sums, a very negative running total is usually bad news. With products, a very negative running product can suddenly become the best thing to keep if the next number is also negative.
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MEDIUMArray, Dynamic Programming, MatrixThe input is a grid of "0" and "1" values. The job is to find the area of the largest square made entirely of 1s.
The part that usually traps people is the word “square.” A brute-force approach tends to ask:
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