Mobile & Web Interview questions

Full-Stack Engineer (TypeScript) interview questions

These Full-Stack Engineer interview questions come from our vetting, where 3.4% of applicants pass 5 gates. You get 20 questions with answer hints, a task, red flags and a scorecard.

Updated: October 2026Remote Pro Ai · Mobile & Web20 questions

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How we assess

Every applicant goes through 5 gates: application, an AI-assisted screen, a live technical assessment scored by a practice lead, a client-fit interview, and reference and credential checks. The questions below come from the live assessment. We score answers on reasoning and trade-offs, not on recalling definitions.

20 technical questions with model-answer hints

Questions 1–⁠10 (role-specific)

  1. Question 1: When do you choose a Server Component over a Client Component in Next.js?

    Model-answer hint

    Data fetching with no interactivity goes server-side; state, effects and browser APIs need the client; mentions bundle size.

  2. Question 2: How do you share types between a Next.js client and a NestJS API?

    Model-answer hint

    Shared package or types generated from OpenAPI / GraphQL schema, plus runtime validation (Zod) at the boundary.

  3. Question 3: Walk us through secure session handling for a web app.

    Model-answer hint

    HttpOnly, Secure, SameSite cookies; short-lived tokens with rotation; CSRF protection; nothing sensitive in localStorage.

  4. Question 4: A list endpoint slows down at 2 million rows. What do you do?

    Model-answer hint

    Reads the query plan, adds indexes, cursor pagination, removes N+1 queries, caches with clear invalidation.

  5. Question 5: How do you run a database migration with zero downtime?

    Model-answer hint

    Expand-and-contract, batched backfills, feature flags or dual writes, every step reversible.

  6. Question 6: REST or GraphQL for a new web and mobile product?

    Model-answer hint

    Weighs caching, over-fetching, tooling and team skills; no dogmatic answer.

  7. Question 7: How do you keep a Next.js page fast on mid-range Android phones?

    Model-answer hint

    Core Web Vitals from field data, image and font optimisation, code splitting, less client JavaScript.

  8. Question 8: What does your test pyramid look like for a full-stack feature?

    Model-answer hint

    Unit tests for logic, integration tests for API plus database, a few Playwright journeys, contract tests between services.

  9. Question 9: How do you handle RTL and Arabic in a shared component library?

    Model-answer hint

    Logical properties, dir attribute, icons mirrored only where meaning requires, locale-aware formatting.

  10. Question 10: Tell us about a production incident you caused and fixed.

    Model-answer hint

    Clear ownership, timeline, root cause, prevention steps, blameless tone.

Questions 11–⁠20 (Mobile & Web core)

  1. Question 11: How do you estimate a feature when requirements are still unclear?

    Model-answer hint

    break it down, name the unknowns, run a short spike, give a range and re-estimate as you learn.

  2. Question 12: How do you integrate national-ID login such as Nafath or UAE Pass safely?

    Model-answer hint

    standard redirect or app-to-app flow, state and nonce checks, secure token storage, expiry and failure paths.

  3. Question 13: What goes into your definition of done for a user-facing feature?

    Model-answer hint

    tests, accessibility checks, analytics events, error states, documentation, feature flag.

  4. Question 14: How do you set and enforce a performance budget?

    Model-answer hint

    Core Web Vitals or startup targets measured on mid-range devices, with CI checks that fail on regression.

  5. Question 15: How do you release safely to millions of users?

    Model-answer hint

    feature flags, staged rollout, crash and error monitoring, fast rollback, store review planning.

  6. Question 16: How do you handle offline or unreliable connectivity?

    Model-answer hint

    local caching, optimistic updates with conflict handling, retries with backoff, clear user feedback.

  7. Question 17: What do you check before an app starts handling payments?

    Model-answer hint

    PCI scope, tokenisation, idempotency, webhooks, reconciliation, provider sandbox tests.

  8. Question 18: How do you keep dependencies and SDKs secure and current?

    Model-answer hint

    lockfiles, automated update pull requests, vulnerability scanning, licence checks, removing unused packages.

  9. Question 19: A design cannot be built as drawn. What do you do?

    Model-answer hint

    early feasibility review, alternatives with trade-offs, design-system components, a shared decision with the designer.

  10. Question 20: How do you review a colleague’s pull request?

    Model-answer hint

    correctness, readability, tests and security; small PRs; clear blocking versus non-blocking comments.

Interviewer tip: Ask for a real example after every answer. Strong candidates describe what they built, what broke and what they changed.

Practical task

Timebox 3 hours take-home or 90 minutes live. Extend a provided Next.js + NestJS + PostgreSQL repository with an “invite teammate” feature: typed API endpoint, migration, email stub, role check, validated UI form and one Playwright test. Scored on correctness, type safety, security defaults, test quality and a 5-minute walkthrough of trade-offs.

How to run it Share the brief 24 hours ahead or run it live. Ask the candidate to talk through trade-offs as they work; reasoning matters more than a finished artefact.

Red flags our practice leads watch for

  • Uses `any` or disables type checks to make the build pass.
  • Stores auth tokens in localStorage without understanding the XSS risk.
  • Cannot explain what a query does at database level.
  • Treats tests and accessibility as somebody else’s job.
  • Universal flag: Vague answers that never reach a concrete system, number or decision the candidate personally owned.

The scorecard we use

Score each dimension 1–⁠4 (1 = no evidence, 2 = partial, 3 = solid, 4 = exceptional):

DimensionWeightWhat a 4 looks like
Technical depth, production judgement and security40%Explains how and why, designs for failure and sensitive data, and knows each tool’s limits
Practical task and problem solving30%Delivers a working solution with tests and documented decisions
Communication and documentation20%Explains clearly to technical and non-technical audiences, in English or Arabic
References and regional context10%References confirm the claimed scope; knows the frameworks and data-residency rules that apply

Pass bar: weighted average of 3.0 out of 4 or higher, with no dimension below 2. The practice lead approves the final result.

Skip the interview loop

Every specialist on our shortlist has already passed this assessment. Working in 4 working days.

Frequently asked questions

Yes. The questions, hints, practical task and scorecard are free to use. They work best when every interviewer scores the same dimensions independently and compares notes afterwards. Ask for a concrete example after each answer, because recalled definitions say little about how someone performs on a live system.

They come from the same live technical assessment, but practice leads rotate and adapt questions so candidates cannot rehearse them. What stays fixed is the scope being tested, the practical task format and the scorecard, which is why the published version is safe to share.

Plan for a technical conversation covering 8 to 10 of the questions, plus the practical task either live or as a short take-home. Use the remaining questions as follow-ups. If you would rather skip the loop, Remote Pro Ai shortlists already-assessed specialists within 48 hours.

Our pass bar is a weighted average of 3.0 out of 4 or higher across the 4 dimensions, with no dimension below 2. Production judgement and security, scored inside the 40% technical dimension, often separate strong candidates from those who interview well but struggle once they own a live system.

The task mirrors the first week of real work in this role and never uses client data. It runs live or as a short take-home of a few hours, and the candidate then explains the trade-offs behind each decision. Reasoning and production habits count for more than a polished final artefact.