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Mobile Performance and Energy Optimization Questions

Comprehensive engineering and operational practices for diagnosing, profiling, and optimizing mobile application performance and device energy consumption at feature and system scale. Candidates should be able to explain strategies to reduce application startup time, minimize main thread work to keep the user interface responsive, and stabilize rendering at target frames per second such as sixty frames per second and one hundred and twenty frames per second to avoid application not responding situations. Core topics include memory management and leak prevention, allocation analysis, preventing crashes and responsiveness regressions, efficient rendering of large feeds, complex gesture and input handling, and efficient handling of large media such as photos and video. Common techniques include lazy loading, request batching, image resizing and compression, caching and batching strategies, offline first synchronization, and efficient background processing and scheduling to limit energy impact. Energy and battery focused optimizations include minimizing sensor usage and location service use when unnecessary, geofencing best practices, network and radio optimizations to reduce radio wake ups, preferring push driven updates over polling where appropriate, and designing background tasks to be energy aware. Candidates should demonstrate familiarity with profiling and instrumentation tools and workflows for mobile platforms, interpreting profiler output to identify central processing unit and memory bottlenecks, measuring and quantifying latency and energy impact, designing architectural and code changes to prevent regressions, reasoning about trade offs between native and cross platform implementations, and defining user perceived performance and energy metrics with continuous monitoring and tests to quantify improvements.

EasyTechnical
0 practiced
How do you measure and report frames per second and frame drops in a mobile application? Describe concrete instrumentation points and metrics (e.g., frame timestamps, frame-time percentiles, jank count), and explain how to surface these in dashboards for engineers and product managers.
HardTechnical
0 practiced
Compare iOS BGTaskScheduler and Android WorkManager/JobScheduler with respect to energy behavior, guarantees, and developer control. Explain how each OS batches background work and how an app should design energy-aware background tasks to play nicely with the system scheduler.
MediumTechnical
0 practiced
You see a profiling summary: allocations per second is 4,800, GC runs every 500ms causing 40-60ms main-thread freezes, and the 95th frame time is 120ms. Walk through how you would identify the most likely sources of allocation churn and list concrete changes to reduce allocations and GC pressure in a mobile UI feed.
MediumTechnical
0 practiced
Given a baseline: cold start 4.0s, warm start 1.2s, and first-render accounts for 65% of cold start time (heavy initialization and synchronous network calls), propose a prioritized plan with concrete engineering changes to reduce cold start by 50% while keeping feature parity. Include quick wins and longer-term refactors.
EasyTechnical
0 practiced
Implement an LRU image cache in Java suitable for an Android app. The cache should support get(key) and put(key, Bitmap, sizeBytes), enforce a maximum capacity in bytes, evict least recently used items until capacity is satisfied, and be thread-safe. Describe or write code for the core data structures and eviction logic.

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