Rank candidates without losing the graph rules
Build mutual-friend recommendations while excluding invalid candidates, preserving deterministic order, and avoiding repeated work.
What you will practice
- Separate candidate generation, scoring, and ranking.
- Use sets deliberately around duplicate paths.
- Verify large sparse graphs against a small reference.
Continue the guided path with Pro
Get this lesson, its focused drills and review guidance, plus explanations, replay, and unlimited native grading. AI usage has a separate allowance.
Sources and scope
The cited employer material supports the preparation format and skills, not these exact tasks, APIs, tests, or timings. Existing nrml exercises retain their own provenance. Reading times and rehearsal budgets are nrml suggestions. Completion records practice and review, not hiring readiness.
- Meta hiring FAQ · practical-format context, not attribution of this exerciseReviewed 2026-10-28