System designLast reported Aug 2026
Design a Grounded Recommendation Chatbot
Design a short-session chatbot grounded in an existing recommender and catalog, with bounded state and explicit refusals.
Open practiceNetflix interview practice
Netflix coding interviews, from implementation to system design. Start with verified evidence.
Practice from 13 Netflix-tagged public coding problems, organized only by the stage, topic, difficulty, and recency metadata FastPrep can verify. The broader public catalog also includes 6 system-design exercises, 1 low-level-design exercise.
- Catalog
- 20 public practice assets (13 coding)
- Start here
- Use the stage named in your invitation
- Evidence
- Phone · Onsite metadata, shown in context
Phone practice path
Phone Screen
Practice solving while explaining assumptions, tradeoffs, and complexity. Problems are ranked by repeated public catalog sightings.
8 Netflix-tagged Phone Screen problems available.
| Company | Problem | Difficulty | Public evidence | Action |
|---|---|---|---|---|
NENetflix | Timed Cache with Sidecar CleanupHash TableDesign | Medium | 2 public reportsLast reported Aug 2026 | Practice |
NENetflix | Capacity-Limited Timed CacheHash TableHeap | Hard | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Dynamic Broadcast TimesGraphShortest Path | Hard | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Count String Pairs With Disjoint CharactersArrayString | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Deduplicate Homepage Content Across RowsArrayHash Table | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Longest Consecutive Identical Character RunString | Easy | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Longest Substring Without Repeating CharactersStringHash Table | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Topological Sort for Ads TasksGraphTopological Sort | Medium | 1 public reportLast reported Jun 2026 | Practice |
Onsite practice path
Onsite
Practice complete solutions and defend the choices behind them. Problems are ranked by repeated public catalog sightings.
| Company | Problem | Difficulty | Public evidence | Action |
|---|---|---|---|---|
NENetflix | Timed Cache with Sidecar CleanupHash TableDesign | Medium | 2 public reportsLast reported Aug 2026 | Practice |
NENetflix | Undo and Redo Command HistoryStackDesign | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Balanced Tree Node ReportTreeDepth-First Search | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Closed Interval Overlap QueriesSortingBinary Search | Medium | 1 public reportLast reported Aug 2026 | Practice |
NENetflix | Movie Billboard RotationDesignSimulation | Medium | 1 public reportLast reported Jul 2026 | Practice |
NENetflix | Command Undo Data StructureStackDesign | Easy | 1 public reportLast reported Apr 2026 | Practice |
01 · Preparation plan
Connect a correct implementation to its system tradeoffs.
The Netflix catalog supports phone and onsite coding practice plus design exercises. Focus on stateful components, explanation, and architecture; this is not an online-assessment guide. This is a suggested practice sequence, not the employer's interview process. Your invitation and recruiter guidance remain the source of truth.
- 01
Implement a stateful component
Use cache, command-history, or content-deduplication practice to make state transitions explicit instead of relying on happy-path examples.
- Write down expiration, capacity, or undo rules before coding.
- Trace operations in a different order and explain the changed result.
- Test one boundary where a locally correct decision breaks a later operation.
- 02
Explain the design under constraints
If a recommendation or advertising design exercise is available, begin with its workload and correctness requirements rather than choosing infrastructure first.
- Separate the user-facing request path from background updates.
- Identify which data can be stale and which decisions must be consistent.
- Discuss overload, duplicate events, and a measurable failure signal.
- 03
Compare implementation and architecture
Return to the coding exercise and explain what would need to change if the same behavior were distributed across machines.
- State the single-process assumption your code relies on.
- Explain how retries or concurrent requests affect that assumption.
- Summarize your design choice and explain when an alternative would be more appropriate.
02 · Broader technical practice
Practice beyond standalone coding questions.
These public Netflix-tagged exercises cover additional technical formats. They are included only when a verified catalog record and a crawlable practice page both exist.
System designLast reported Aug 2026
Design a Personalized Recommendation System
Design event ingestion, candidate retrieval, ranking, and serving for a bounded personalized user-item catalog.
Open practiceSystem designLast reported Aug 2026
Design a Competitive Ad Exclusion Service
Design a sharded serving-time service that prevents configured competitor ads from appearing too close together for one viewer and movie.
Open practiceLow-level designLast reported Aug 2026
Design a Parking Lot with Flexible Fees
Model a parking journey whose versioned pricing policies support complex hourly fees without destabilizing active sessions.
Open practiceSystem designLast reported Aug 2026
Design Advertising Order Lifecycle Tracking
Design the data model and services that track an advertising order's versioned configuration, lifecycle, delivery, and spend progress.
Open practiceSystem designLast reported Aug 2026
Design an Advertising Frequency-Capping Service
Design a low-latency service that prevents one viewer from receiving an advertisement more often than its configured caps allow.
Open practiceSystem designLast reported Jul 2026
Design a Machine-Learning Job Scheduler
Design a multi-tenant scheduler that allocates scarce compute to training and batch-inference jobs, monitors leases, and recovers safely from worker crashes.
Open practice03 · Evidence boundary
What “Netflix interview questions” means here.
It means practicing transferable implementation, testing, and technical reasoning with public catalog assets FastPrep tags to Netflix. It does not mean FastPrep has access to the company's assessments or any private interview bank.
Repeated public sightings and last-reported dates can help you prioritize practice, but they cannot predict the questions, format, or platform in a specific interview.
- Only Netflix-tagged FastPrep catalog problems appear above.
- Stage labels and report dates remain visible as context.
- No official, private, leaked, or proprietary questions are claimed.
04 · Plain answers
Netflix interview practice questions, answered plainly.
This page owns technical-practice intent for Netflix. Hiring activity, timelines, and market signals remain on the separate hiring-insights page.
01Is this a Netflix online-assessment guide?
No. This page focuses on coding and system-design practice, not a claimed Netflix OA format. The public catalog currently supports 13 coding problems, 6 system-design exercises, and 1 low-level-design exercise. Reported phone and onsite labels describe practice context, not a fixed interview sequence.
02Is FastPrep affiliated with Netflix?
No. FastPrep is an independent interview-preparation product and is not affiliated with Netflix. The page uses FastPrep's public practice catalog and does not claim official, private, leaked, or proprietary employer questions.
03How are Netflix problems assigned to interview stages?
Stage labels come from FastPrep's public problem metadata. A problem can carry more than one reported stage, and hiring processes can change by role, level, location, and date. Treat the labels as preparation context, not a guarantee.
04Which Netflix interview stage should I practice first?
Start with the stage named in your invitation or recruiter message. If no stage is known, use the largest available set to build general problem-solving fluency, then rehearse explanation and testing separately.
05Does this page predict the Netflix interview process?
No. Public catalog counts, stage tags, and last-reported dates can help prioritize practice, but they cannot predict a specific interview's questions, sequence, timing, or platform.
Your next stage, made concrete
Pick a problem. Solve it end to end.
Start with the stage named in your invitation, then use public evidence as context—not as a promise of what you will be asked.
Open the practice set