## Problem Given a list of purchase events for a single user, each as `(item_id, timestamp)`, determine the most frequent consecutive purchase pattern of length `k`. A pattern is a sequence of exactly `k` distinct items bought in consecutive transactions (no gaps). Return the pattern as a list of item IDs in the order they were purchased. If there is a tie, return the lexicographically smallest sequence. ```python def most_frequent_pattern(purchases: list[tuple[int, int]], k: int) -> list[int]: ... ``` **Example:** ``` purchases = [(1,100),(2,200),(3,300),(1,400),(2,500),(3,600),(1,700),(4,800)] k = 2 Output: [1, 2] # (1->2) appears twice; (2->3) appears twice -> tie -> lex smaller ``` **Constraints:** `1 <= k <= len(purchases)`, timestamps are strictly increasing. ## Follow-ups - How does your approach change if the same item can appear multiple times within a pattern? - Extend to find patterns across multiple users and return patterns seen by at least `m` users. - What is the time complexity? Can you do it in O(n) for k=2?

InterviewDB Question

Purchase Pattern Detection - Coding Interview

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Question Details

Problem Given a list of purchase events for a single user, each as (item_id, timestamp), determine the most frequent consecutive purchase pattern of length k. A pattern is a sequence of exactly k distinct items bought in consecutive transactions (no gaps). Return the pattern as a list of item IDs in the order they were purchased. If there is a tie, return the lexicographically smallest sequence. Example: Constraints: 1 <= k <= len(purchases), timestamps are strictly increasing. Follow-ups How do…

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About This Question

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

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

Topics

Coding Onsite Phone

About Affirm Interview Reports

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

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

During Your Affirm 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 Affirm 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.