## Problem You are given a multi-line string representing a restaurant menu. Sections are indicated by lines in ALL CAPS, and items follow as `item_name: $price`. Parse it into a nested dictionary: `{section_name: [{name: str, price: float}]}`. ```python def parse_menu(raw: str) -> dict[str, list[dict]]: pass ``` **Example:** ``` raw = """ APPETIZERS Spring Rolls: $6.50 Soup of the Day: $4.00 MAIN COURSE Grilled Salmon: $18.00 Pasta Primavera: $14.50 """ -> { "APPETIZERS": [{"name": "Spring Rolls", "price": 6.50}, {"name": "Soup of the Day", "price": 4.00}], "MAIN COURSE": [{"name": "Grilled Salmon", "price": 18.00}, {"name": "Pasta Primavera", "price": 14.50}] } ``` ## Follow-ups 1. How do you handle items that appear before any section header? 2. What if prices use different currencies or formats (e.g., `EUR 6,50`)? How do you make price parsing robust? 3. How would you extend this to support sub-sections (indented ALL CAPS headers)? 4. If this parser is used in a scraping pipeline, what logging and error recovery would you add for malformed input lines?

InterviewDB Experience

Menu Parser: Parse a Hierarchical Restaurant Menu from Raw Text into a Structured Object

phone

Interview Experience

Problem You are given a multi-line string representing a restaurant menu. Sections are indicated by lines in ALL CAPS, and items follow as item_name: $price. Parse it into a nested dictionary: {section_name: [{name: str, price: float}]}. Example: Follow-ups How do you handle items that appear before any section header? What if prices use different currencies or formats (e.g., EUR 6,50)? How do you make price parsing robust? How would you extend this to support sub-sections (indented ALL CAPS hea…

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About This Question

This is a candidate experience report from a cloudkitchens interview during the phone round.

It covers the following topics: Coding, Phone, Strings .

Topics

Coding Phone Strings

About Cloudkitchens Interview Reports

This question was reported by a candidate who interviewed at Cloudkitchens. 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 Cloudkitchens are the higher-signal extractions to take from this report.

For broader preparation context, the Cloudkitchens 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 Cloudkitchens reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Cloudkitchens 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 Cloudkitchens 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.