Coding (Platform)

React Native: Apply a sliding rate window for large list request stream

A mobile workflow diverges after backgrounding, reconnecting, or replaying queued work. The affected area is large list.

Repair the large list request stream implementation so it enforces a per-key sliding window and reports allowed and rejected requests.

Evidence

Affected surface: React Native / large list.
Observed failure family: apply a sliding rate window.
Scope policy: cohort must equal stable.

What this task practices

This coding (platform) exercise trains the same sequence expected in an interview: understand the prompt, make assumptions explicit, produce a concrete answer, and explain the decisions behind it. The task is marked medium difficulty. The private workspace adds the tools required by this round, such as recording, code execution, diagrams, evidence panels, or structured notes. It also preserves the attempt so later feedback can be compared with previous work. Evaluation is based on task-specific criteria and the candidate seniority selected in the preparation path. Public pages never expose the reference solution, hidden tests, evaluator instructions, or another candidate’s work.