## Problem Implement a fixed-capacity circular buffer queue that supports `enqueue`, `dequeue`, `peek`, and `is_full`/`is_empty` operations. When full, the enqueue operation should overwrite the oldest element (ring buffer semantics). The buffer should be O(1) for all operations. ```python class BufferQueue: def __init__(self, capacity: int): ... def enqueue(self, item) -> None: ... def dequeue(self): # returns item or raises if empty def peek(self): # returns next item without removing def is_full(self) -> bool: ... def is_empty(self) -> bool: ... def __len__(self) -> int: ... ``` **Example:** ``` bq = BufferQueue(3) bq.enqueue(1); bq.enqueue(2); bq.enqueue(3) bq.is_full() -> True bq.enqueue(4) # overwrites oldest: [2,3,4] bq.dequeue() -> 2 bq.dequeue() -> 3 bq.is_empty() -> False ``` ## Follow-ups 1. How do you distinguish a full buffer from an empty buffer when using only head and tail pointers (the classic off-by-one problem)? 2. What is the difference between overwrite-on-full semantics and block-on-full semantics, and where is each used? 3. How would you make this thread-safe for a single producer and single consumer without a mutex (lock-free SPSC queue)? 4. How is this data structure used in audio/video streaming pipelines (e.g., jitter buffers)?

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Buffer Queue: Implement a Fixed-Capacity Circular Buffer Queue with Overflow Policy

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Problem Implement a fixed-capacity circular buffer queue that supports enqueue, dequeue, peek, and is_full/is_empty operations. When full, the enqueue operation should overwrite the oldest element (ring buffer semantics). The buffer should be O(1) for all operations. Example: Follow-ups How do you distinguish a full buffer from an empty buffer when using only head and tail pointers (the classic off-by-one problem)? What is the difference between overwrite-on-full semantics and block-on-full sema…

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How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Rubrik reports consistently are the ones worth investing in; one-off niche problems are not.

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The single most predictive failure mode in Rubrik reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.