## Problem You have a sorted list of GIF objects `[{"id": str, "trending_score": int}]`. Implement a `paginate` function that returns a page of results given `offset` and `limit`. If the caller passes `cursor` (the last-seen id) instead of `offset`, resolve the offset from the cursor first. ```python def paginate( gifs: list[dict], offset: int = 0, limit: int = 10, cursor: str | None = None ) -> dict: # Returns {"data": [...], "next_cursor": str | None} pass ``` **Example:** ``` gifs = [{"id": "a", ...}, {"id": "b", ...}, {"id": "c", ...}] paginate(gifs, offset=1, limit=2) -> {"data": [{"id": "b"}, {"id": "c"}], "next_cursor": None} ``` ## Follow-ups 1. Offset-based vs. cursor-based pagination -- when does each break down at scale? 2. What happens if a new GIF is inserted before the cursor position between two page fetches? How do you maintain consistency? 3. Trending score changes frequently. How do you prevent items from appearing twice or being skipped across pages? 4. Implement server-side caching so repeated identical page requests don't re-sort the list.

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GIF Pagination: Return the Correct Page of GIF Results Given Offset and Limit Parameters

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Problem You have a sorted list of GIF objects [{"id": str, "trending_score": int}]. Implement a paginate function that returns a page of results given offset and limit. If the caller passes cursor (the last-seen id) instead of offset, resolve the offset from the cursor first. Example: Follow-ups Offset-based vs. cursor-based pagination -- when does each break down at scale? What happens if a new GIF is inserted before the cursor position between two page fetches? How do you maintain consistency?…

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

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

During Your Sofi 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 Sofi 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.