## Problem You have a log of HTTP requests, each with a timestamp (seconds) and a status: `"success"` or `"dropped"`. Find all contiguous time windows of length `W` seconds where the drop rate exceeds threshold `T` (0.0 to 1.0). Return the start times (inclusive) of all such windows. ```python from typing import List, Tuple def high_drop_windows( requests: List[Tuple[int, str]], W: int, T: float ) -> List[int]: # requests: [(timestamp, status), ...] sorted by timestamp # W: window size in seconds # T: drop rate threshold (exclusive) # return: sorted list of window start times pass ``` **Example:** ``` requests = [(0,"dropped"),(1,"dropped"),(2,"success"),(5,"dropped"),(6,"dropped")] W=3, T=0.5 Window [0,3): 2 dropped / 3 total = 0.67 > 0.5 -> include start=0 Window [4,7): 2 dropped / 2 total = 1.0 > 0.5 -> include start=4 Output: [0, 4] ``` ## Follow-ups 1. What if requests are NOT sorted by timestamp — what preprocessing is needed? 2. How would you adapt this to a live streaming data source using a sliding window? 3. How do you handle windows that contain zero requests (divide-by-zero case)? 4. In an alerting system, how would you avoid duplicate alerts for overlapping windows?

InterviewDB Question

Dropped Requests: Find Time Windows with Unacceptably High Request Drop Rates

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Question Details

Problem You have a log of HTTP requests, each with a timestamp (seconds) and a status: "success" or "dropped". Find all contiguous time windows of length W seconds where the drop rate exceeds threshold T (0.0 to 1.0). Return the start times (inclusive) of all such windows. Example: Follow-ups What if requests are NOT sorted by timestamp — what preprocessing is needed? How would you adapt this to a live streaming data source using a sliding window? How do you handle windows that contain zero requ…

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About This Question

This is a reported interview question from a snowflake interview during the phone round.

It covers the following topics: Sliding Window, Coding, Phone, Onsite .

Topics

Sliding Window Coding Phone Onsite

About Snowflake Interview Reports

This question was reported by a candidate who interviewed at Snowflake. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

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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 Snowflake reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Snowflake Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Snowflake 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.