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Swift Collections: Array, Set, Dictionary
Swift's three collections are different access patterns, not just different APIs. Arrays keep order, Sets enforce uniqueness, and Dictionaries map keys to values. Picking an Array for uniqueness checks turns membership from O(1) into O(n) scans.
Swift Basic Types: Value Semantics by Default
Swift's basic types are value type structs, so assignment copies, not shares, a reference. You feel this when passing Strings into functions or choosing Int over Double. The footgun is treating them as free to copy; large values cost memory and speed.
Apple's Natural Language Framework
Apple's Natural Language Framework turns raw strings into structured meaning on-device without network calls. Tokenize queries or extract entities from user text locally. It is not infallible; heavy synchronous tagging on the main thread freezes your UI.
UserNotifications Framework: iOS Alert Gatekeeper
UserNotifications is the gatekeeper between your app and the lock screen. You use it to request permission and schedule local or push alerts on iOS. Send a notification before authorization and the system silently drops it.
Result Builders: Declarative Swift DSLs
Result builders teach the compiler to fold expressions into one combined value, enabling declarative DSLs. SwiftUI relies on them to compose stacked views. Type errors surface on compiler-generated boilerplate instead of your original code.
Swift Enums: Type-Safe Choice Modeling
A Swift enum is a closed menu of possibilities the compiler tracks exhaustively. Use it to replace string constants or model a network result state. Adding a case without updating every switch breaks compile-time safety if you rely on a default clause.
Explain the difference between statistical and practical significance
Define statistical vs practical significance; note large samples make tiny effects significant; give a real example.
Build a system to measure viral coefficient and attribute invites to signups
Tests if you can map K=i×c to logged events and resilient pipeline. Good answers define invite_sent, click, signup events with referral tokens; sketch stream joins; and flag cross-device and organic attribution gaps. Red flag: assuming perfect attribution.
Design a referral system: data models, APIs, attribution, self-referral prevention
Tests data modeling with fraud guardrails and idempotent rewards. Cover: Users with nullable referred_by, ReferralEvents state table, async ledger attribution, and device-fingerprint self-referral blocks. Red flag: bare integer credit with no audit trail.
Describe cluster or switchback randomization for network-effect A/B tests.
Tests SUTVA violation, cluster-switchback tradeoffs. Outline: cluster (geo, teams) or switchback (time slices) isolation; cover pipeline changes, 10x inflation, and correlated error. Red flag: user-level randomization with post-hoc fixes or ignoring spillover.
Compare server-side and client-side experimentation architectures
This tests rendering-layer architecture. A strong answer contrasts server-side zero-flicker and algorithm tests against client-side marketer agility and SEO safety, mapping each to release cycles. A red flag is claiming one approach dominates every dimension.
How do you prevent concurrent onboarding and navigation experiments from polluting results?
This tests experiment isolation via layer-based traffic allocation. A strong answer covers hashing users into independent layers with one variant per layer, and assigning each experiment to a distinct layer.
How do you systematically manage and pay down experiment debt?
Tests sustainable velocity through experiment lifecycle hygiene. Strong answers cover isolated experiment directories, TTLs on feature flags, and recurring cleanup sprints. Red flag: banning experiments or treating all experiment code as permanent.
What experiment artifacts and metrics do you present to decide shipping?
This tests structured experiment discipline. A strong answer covers the pre-approved design doc, scorecard results for primary goal and guardrail metrics plus secondary breakdowns versus the hypothesis, and duration context.
Lifecycle of a feature flag experiment from creation to cleanup
Tests operational rigor across the full flag lifecycle. A strong answer covers six stages: SDK instrumentation with event tracking, phased rollout, monitored experiment, ship/kill decision, and code cleanup.
How do you mitigate peeking in experiment infrastructure?
Lock results behind minimum samples; auto-correct via sequential testing; hide early metrics and require stop approval.
Design a pre-aggregation architecture for low-latency experiment results
Tests OLAP-at-scale trade-offs. Strong answers design streaming rollups into a real-time OLAP store, use partial cubes for high-cardinality dimensions, and retain raw events.
Design a system to reduce large client-side experiment payload size
Tests edge evaluation and payload compression. Use server-side pre-evaluation or edge nodes sending only assigned variants; compact bucketing indexes or Bloom filters; lazy-load noncritical experiments. Never do full client-side evaluation of every flag rule.
Compare PostgreSQL versus a columnar warehouse for raw event data
Contrast row vs column storage, compression, and scan speed; note Postgres suits OLTP and point lookups while columnar stores excel at aggregations.
Should we build or buy an A/B testing platform?
This tests build-vs-buy judgment for experimentation infrastructure. Strong answers cover build for warehouse metrics and cache control; buy for proven stats and front-end speed. Red flag: answering with cost alone or assuming in-house is always superior.