Stop Sequence: Validate and Reorder Transit Stops Along a Route
Interview Experience
Problem
You are given a list of bus stops with their positions along a route (in km from route start) and a list of stop names in a claimed order. Validate whether the claimed order is consistent with the physical positions (i.e., monotonically increasing distance). If not,
return the corrected order sorted by distance.
python
from dataclasses import dataclass
@dataclass
class Stop:
name: str
distance_km: float
def validate_stop_sequence(
stops: list[Stop],
claimed_order: list[str] # stop names
) -> tuple[bool, list[str]]:
**Returns** (is_valid, corrected_order)
pass
Example:
stops = [Stop("A",0), Stop("B",5), Stop("C",10), Stop("D",15)]
claimed_order = ["A", "C", "B", "D"]
-> (False, ["A", "B", "C", "D"])
claimed_order = ["A", "B", "C", "D"]
-> (True, ["A", "B", "C", "D"])
Follow-ups
- How do you handle stops that are not in the provided
stopslist -- raise an error or skip? - What if two stops are at the same distance (e.g., two doors of the same station)? How do you break ties in the corrected order?
- For a circular route (last stop connects back to first), how does the validation logic change?
- How would you compute the minimum number of swaps needed to transform the claimed order into the correct order?
Full Details
Problem
You are given a list of bus stops with their positions along a route (in km from route start) and a list of stop names in a claimed order. Validate whether the claimed order is consistent with the physical positions (i.e., monotonically increasing distance). If not,
return the corrected order sorted by distance.
python
from dataclasses import dataclass
@dataclass
class Stop:
name: str
distance_km: float
def validate_stop_sequence(
stops: list[Stop],
claimed_order: list[str] # stop names
) -> tuple[bool, list[str]]:
**Returns** (is_valid, corrected_order)
pass
Example:
stops = [Stop("A",0), Stop("B",5), Stop("C",10), Stop("D",15)]
claimed_order = ["A", "C", "B", "D"]
-> (False, ["A", "B", "C", "D"])
claimed_order = ["A", "B", "C", "D"]
-> (True, ["A", "B", "C", "D"])
Follow-ups
- How do you handle stops that are not in the provided
stopslist -- raise an error or skip? - What if two stops are at the same distance (e.g., two doors of the same station)? How do you break ties in the corrected order?
- For a circular route (last stop connects back to first), how does the validation logic change?
- How would you compute the minimum number of swaps needed to transform the claimed order into the correct order?
About This Question
This is a candidate experience report from a nuro interview during the phone round.
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This question was reported by a candidate who interviewed at Nuro. 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.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Nuro are the higher-signal extractions to take from this report.
For broader preparation context, the Nuro interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
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 Nuro reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Nuro 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 Nuro 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.