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Sample Vetting Questions

Example questions at senior and principal level for Mobile Developer, with what a strong answer covers. They show the depth TeamStation AI tests. They are not a real candidate interview.

React

1. Principal level, Mental Model. A list of 5,000 rows re-renders on every keystroke in a search box. How do you find out why, and in what order do you try fixes?

   A strong answer covers: The profiler to see what rendered and why; Moving state closer to where it is used before adding memoization; Virtualizing the list; Marking the list update as non urgent.

2. Principal level, Mental Model. How is server state different from client state, and what goes wrong when a team puts both in one global store?

   A strong answer covers: Caching, staleness and invalidation; Request deduplication; Optimistic updates and rollback; A clear owner for each piece of data.

3. Principal level, Mental Model. How do you split a 300 component app into features so teams ship without stepping on each other?

   A strong answer covers: Feature boundaries with a small public surface; Import rules enforced by lint; Shared UI kit ownership; Dependencies that only point one way.

4. Staff level, Mental Model. What do you test at the component level, what end to end, and what do you stop testing?

   A strong answer covers: Behavior a user can see, not internals; Contracts with the server; The cost of flaky tests; Tests that duplicate each other.

5. Principal level, Cognitive Load. An error shows up for 0.3 percent of users, only on one Android browser. How do you find it?

   A strong answer covers: Release and device tags in error tracking; Source maps; Breadcrumbs or session replay; A flag to switch the change off.

6. Principal level, Accuracy. An AI assistant wrote a 600 line component. What do you check first?

   A strong answer covers: Behavior against the requirement; Effects and cleanup; Accessibility; Bundle cost and tests.

Node.js

7. Principal level, Mental Model. An API answers fast on average but stalls for 800 ms every few minutes. How do you tell a blocked event loop from a slow dependency?

   A strong answer covers: Event loop delay measurement; CPU profile of the process; Separating synchronous work from waiting on I/O; Garbage collection pauses as a third cause.

8. Principal level, Mental Model. A downstream service starts timing out and your Node service falls over. Walk through what happens and how you stop it spreading.

   A strong answer covers: Timeouts on every call; Retries with a budget and jitter; Circuit breaking and bulkheads; Queue growth and memory.

9. Principal level, Cognitive Load. A job queue worker processes some jobs twice. List the causes and the design that makes duplicates harmless.

   A strong answer covers: At least once delivery; Visibility timeouts and slow jobs; Idempotent handlers; Dead letter handling.

10. Principal level, Accuracy. A critical vulnerability lands in a package three levels down your dependency tree. What do you do in the first hour?

    A strong answer covers: Is the code path reachable; Lockfile and tree inspection; Patch, override or mitigate; Telling the right people.

11. Principal level, Cognitive Load. CPU is flat, but p99 latency doubled after a deploy three hours ago. Give your first five actions.

    A strong answer covers: Compare versions; Event loop delay and dependency latency; Roll back or hold and why; Say what you know and do not know.

12. Principal level, Accuracy. An AI assistant wrote a new service in 400 lines. What do you check before you read it?

    A strong answer covers: Tests and what they prove; Dependencies it added; Error paths and timeouts; Behavior against the requirement.

Python

13. Principal level, Mental Model. A CPU bound Python service does not get faster when you add threads. Why, and what are your real options?

    A strong answer covers: The global interpreter lock and what it limits; Processes, native extensions or another service; I/O bound work being different; Newer free threaded builds and their trade offs.

14. Principal level, Mental Model. How do you introduce type checking into a 400,000 line untyped codebase without stopping delivery?

    A strong answer covers: Start at boundaries and new code; A gradual strictness ratchet in CI; Public interfaces first; What types will not catch.

15. Principal level, Cognitive Load. A nightly pipeline now takes 9 hours and used to take 2. How do you find the cause and make it safe to rerun?

    A strong answer covers: Stage timings and data volume growth; Skew and large joins; Idempotent writes; Checkpoints and partial reruns.

16. Principal level, Cognitive Load. A Django or FastAPI service has an N+1 query problem in one endpoint and the team added caching instead. What do you say?

    A strong answer covers: Fix the query first; Caching hides but does not remove cost; Invalidation cost; A test that guards the query count.

17. Principal level, Accuracy. Coverage is 92 percent and a serious bug still shipped. What does that show, and what do you add?

    A strong answer covers: Coverage measures lines run, not behavior; Property based or mutation testing; Tests at the boundaries; Production signals.

18. Principal level, Accuracy. An AI assistant wrote a data processing script that runs fine on a sample. What do you check before it touches production data?

    A strong answer covers: Edge cases in the data; Memory and runtime at full size; Idempotency and rollback; Who can see the output.

The five Axiom Cortex axes, with one example each

• Procedural Knowledge. Walk me through finding which component causes a slow keystroke in a large form. Looks for: Order of steps; What each step proves; What you do if the profiler shows nothing.
• Mental Model. Why can one slow synchronous function make unrelated requests slow? Looks for: One thread runs all callbacks; Waiting on I/O versus computing; Where heavy work should go.
• Accuracy. What does a Python generator hold in memory, and when does it surprise people? Looks for: Correct limits; One pass only; Says when it is unsure.
• Clarity. Explain to a designer why the page flashes old data before new data appears. Looks for: One cause named; One decision for the listener; Plain words.
• Cognitive Load. Latency is up, a deploy just went out, an upstream partner is also failing and logs are noisy. What do you do first, second and third? Looks for: A clear order; Facts kept apart from guesses; Stays correct as facts change.

Questions answered on this route