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Explain UIKit MVC and the Massive View Controller problem
Model holds data, View displays it, Controller mediates between them; the view controller accretes networking, parsing, and logic into a Massive View Controller.
Describe the iOS app lifecycle and SceneDelegate methods
Not running, inactive, active, background, suspended; SceneDelegate handles sceneDidBecomeActive, sceneWillResignActive, sceneDidEnterBackground, sceneWillEnterForeground.
How do you build a custom SwiftUI Layout?
Conform to Layout, implement sizeThatFits to report the container size for a proposal, and placeSubviews to position each subview using its measured size.
How does Auto Layout resolve constraints?
Auto Layout solves a system of prioritized linear equations; intrinsicContentSize is a view's natural size; hugging resists growing, compression resistance resists shrinking.
What is @autoclosure and when is it useful?
@autoclosure wraps an argument expression in a closure so it evaluates lazily only if used, enabling clean APIs like assert and the ?? operator.
Why can't you subscript a Swift String with an Int?
Characters are extended grapheme clusters of variable byte width, so integer offsets are not O(1) or meaningful; String.Index is an opaque position you advance via the collection.
How does Swift's switch differ from C's switch?
Switches must be exhaustive, there is no implicit fallthrough between cases, and cases can match ranges, tuples, and bind values.
Test Plans (XCTestPlan)
A Test Plan is an Xcode configuration file decoupling which tests run from how they run, letting one scheme execute many configurations: localizations, sanitizers, randomized order, and repetition, all without editing the scheme.
Designing a self-serve experimentation platform
SDK with sane defaults, automated pre-launch validation, sample-ratio and guardrail-metric checks.
Sensitivity analysis on a growth model
Perturb each input by a normalized amount, measure the change in the long-term output, and use elasticities or global methods to rank drivers.
Build a SaaS churn prediction model
Define churn and the prediction window, engineer usage-trend and tenure features, try logistic regression then gradient-boosted trees, and evaluate on class-imbalanced metrics.
Peeking in A/B tests and how to mitigate it
Peeking is checking significance repeatedly and stopping at the first significant result, which inflates false positives; mitigate with fixed sample sizes or sequential…
Cold-start to personalized feed transition
Start with popularity or onboarding-declared interests, collect implicit signals like dwell and clicks, then blend toward personalized as confidence grows.
Dynamic personalized onboarding architecture
A segmentation pipeline, a serving layer choosing task order per segment, an experimentation engine, and a feedback loop measuring activation.
Cross-platform stateful onboarding sync
Store onboarding state server-side keyed to the user, expose idempotent step-completion APIs, and push updates to other clients.
Implement a welcome-message A/B test
Deterministic hash of a stable ID for sticky assignment, conditional rendering of the personalized variant, and exposure plus click tracking keyed to the same ID.
Detect fraudulent app installs
Click-to-install timing distributions, device and IP fingerprints, post-install engagement, and attribution anomalies.
Explain deferred deep linking flow
Capture link payload server-side at click, route to the store, then match the new install to the click on first launch to route the user.
Design a unique referral code system
A unique DB constraint as the source of truth, generation via random retry or an encoded counter, and collision handling.
Capture UTM params and attribute on signup
Parse UTMs on landing, persist them in a cookie tied to an anonymous ID, then stamp them onto the account at signup.