## Problem Given integers `lo`, `hi`, and a digit rule, return all integers in `[lo, hi]` (inclusive) that satisfy the rule. The rule: a number "belongs" if the sum of its digits is divisible by `k`. ```python def numbers_that_belong(lo: int, hi: int, k: int) -> list[int]: pass ``` ``` Input: lo=1, hi=30, k=5 Output: [5, 10, 14, 19, 23, 28] # Digit sums: 5->5, 10->1... wait, 1+0=1, not 5. # 5->5 (div by 5), 14->5 (div by 5), 19->10 (div by 5), 23->5, 28->10 # Let me recheck: 10->1, skip. 15->6, skip. 5->5 yes, 14->5 yes, 19->10 yes, 23->5 yes, 28->10 yes. Output: [5, 14, 19, 23, 28] Input: lo=10, hi=20, k=3 Output: [12, 15, 18, 21] # 21 > 20, so [12, 15, 18] # 12->3, 15->6, 18->9 all div by 3. ``` ## Follow-ups 1. For very large ranges (lo=1, hi=10^18), brute force is too slow. How does digit DP solve this? 2. Describe the digit DP state: `dp[position][current_digit_sum_mod_k][is_tight]`. 3. How do you handle the count vs. enumeration distinction at large scale? 4. Extend to a rule where the product of digits (ignoring zeros) must be divisible by `k`.

InterviewDB Experience

Numbers That Belong: Find All Integers in a Range Satisfying a Digit Rule

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Interview Experience

Problem Given integers lo, hi, and a digit rule, return all integers in [lo, hi] (inclusive) that satisfy the rule. The rule: a number "belongs" if the sum of its digits is divisible by k. Follow-ups For very large ranges (lo=1, hi=10^18), brute force is too slow. How does digit DP solve this? Describe the digit DP state: dp[position][current_digit_sum_mod_k][is_tight]. How do you handle the count vs. enumeration distinction at large scale? Extend to a rule where the product of digits (ignoring…

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About This Question

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

It covers the following topics: Coding, Phone, Dynamic Programming .

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Coding Phone Dynamic Programming

About Squarespace Interview Reports

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

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

During Your Squarespace 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 Squarespace 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.