Understanding how data structure and algorithm choices affect mobile applications and constrained environments. Candidates should discuss memory usage CPU and battery implications of different structures, garbage collection pressure and allocation patterns, and strategies to reduce memory and compute overhead on mobile devices. This includes preferring memory efficient structures avoiding unnecessary copies reducing allocation churn using pooling or compact representations and reasoning about algorithmic complexity in the context of limited RAM and CPU and thermal and battery constraints.
MediumTechnical
32 practiced
Scenario: You need to display a smooth scrolling list with thousands of server-provided items on low-memory devices. Describe an end-to-end approach: backend pagination, client-side paging/virtualization, data windowing strategy, lightweight in-memory representations for visible items, prefetching thresholds, and approaches to handle search and filtering without pulling the entire dataset into memory.
MediumSystem Design
29 practiced
Compare using SQLite (disk-backed, paged) versus an in-memory store (e.g., HashMap or in-memory Realm) for caching up to ~100MB user data on mobile devices. Analyze startup latency, memory pressure, query performance, battery/IO costs, durability, and recommended hybrid strategies for low-end devices.
HardSystem Design
34 practiced
System design: Design an offline-first synchronization algorithm for a mobile application that supports local edits while minimizing CPU and battery usage during sync. Requirements: support conflict resolution, incremental/delta sync, efficient batching and backoff, resume after intermittent connectivity, and minimal data transfer. Describe data model, change tracking, conflict policy, scheduling strategy and how to keep syncs energy-efficient.
MediumTechnical
39 practiced
Discuss concurrency and asynchronous patterns used in mobile applications (Kotlin coroutines, RxJava, plain threads, Grand Central Dispatch on iOS). Compare their CPU, memory and battery impacts, how they allocate resources, scheduling costs (threads vs lightweight tasks), and recommend best practices to avoid excessive wakeups, thread thrashing, and allocation churn in concurrent code.
HardTechnical
29 practiced
Scenario: Crash reports from low-end Android devices (API 19) show OOM when opening a large dataset in the app. Outline a diagnostic plan to reproduce and triage the problem (collecting heap dumps, Allocation Tracker, emulator configs), list probable root causes (large images, uncontrolled caches, off-heap fragmentation), and recommend prioritized fixes that minimize regressions across the device matrix.
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