## Problem Implement a rate-limited API client wrapper. The client must respect a maximum of `rate_limit` requests per second. If a request fails with a retryable error (HTTP 429 or 5xx), retry up to `max_retries` times using exponential backoff. Non-retryable errors (4xx except 429) should raise immediately. ```python import time class RateLimitedClient: def __init__(self, rate_limit: int, max_retries: int = 3): pass def get(self, url: str) -> dict: """Make a GET request. Returns parsed JSON response.""" pass def post(self, url: str, payload: dict) -> dict: pass ``` ``` Example behavior: client = RateLimitedClient(rate_limit=10, max_retries=3) # 10 requests/sec max enforced internally # On 429: wait 2^attempt seconds, retry # On 500: same retry logic # On 404: raise immediately ``` ## Follow-ups 1. How would you implement a token bucket or leaky bucket to enforce the rate limit? 2. How does jitter in exponential backoff reduce thundering herd problems? 3. How would you make the client thread-safe for concurrent callers? 4. Extend to support request queuing: callers submit requests and await results asynchronously.

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API Integration: Rate-Limited HTTP Client with Retry and Backoff

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Problem Implement a rate-limited API client wrapper. The client must respect a maximum of rate_limit requests per second. If a request fails with a retryable error (HTTP 429 or 5xx), retry up to max_retries times using exponential backoff. Non-retryable errors (4xx except 429) should raise immediately. Follow-ups How would you implement a token bucket or leaky bucket to enforce the rate limit? How does jitter in exponential backoff reduce thundering herd problems? How would you make the client t…

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This is a candidate experience report from a brex interview during the onsite round.

It covers the following topics: Coding, Onsite .

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API Integration: Rate-Limited HTTP Client with Retry and Backoff

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