Performance & Optimization Interview Questions

Key factors include: 1) Number of test cases and complexity, 2) Network requests and response times, 3) Test retry configurations, 4) Browser instance management, 5) Resource usage in the application, 6) Command timeout settings, 7) Test parallelization setup.

Test execution can be optimized by: 1) Implementing proper test parallelization, 2) Using API calls instead of UI interactions when possible, 3) Optimizing waiting strategies, 4) Implementing proper caching, 5) Reducing unnecessary browser reloads.

Caching helps improve performance by: 1) Storing browser session data, 2) Caching network responses, 3) Maintaining test artifacts, 4) Preserving application state between tests, 5) Reducing setup time for repeated operations.

Timeouts impact performance through: 1) Command execution waiting times, 2) Network request timeouts, 3) Page load timeouts, 4) Assert timeouts, 5) Default vs custom timeout configurations. Proper timeout management is crucial for optimal performance.

Screenshots and videos affect performance by: 1) Increasing disk I/O, 2) Consuming memory resources, 3) Adding processing overhead, 4) Increasing test artifacts size. Consider selective capture and proper configuration for optimal performance.

Browser management impacts performance through: 1) Browser launch time, 2) Memory usage, 3) Resource allocation, 4) Test isolation overhead, 5) Cache management. Proper browser configuration and reuse strategies can improve performance.

Test isolation affects performance through: 1) State reset requirements, 2) Setup/teardown overhead, 3) Browser reloads, 4) Database cleanup operations, 5) Session management. Balance isolation needs with performance considerations.

Network requests affect performance through: 1) Response waiting times, 2) Request retry overhead, 3) Payload size processing, 4) Connection management, 5) Stubbing/mocking overhead. Optimize network handling for better performance.

Logging affects performance through: 1) I/O operations, 2) Memory usage for log storage, 3) Console output processing, 4) Log file management, 5) Reporting overhead. Implement appropriate logging levels and strategies.

Retries impact performance through: 1) Additional test execution time, 2) Resource usage during retries, 3) State reset requirements, 4) Logging overhead, 5) Result aggregation. Balance retry strategies with performance needs.

Data optimization strategies include: 1) Efficient data creation/cleanup, 2) Data caching mechanisms, 3) Bulk operations for setup/teardown, 4) Optimized fixture loading, 5) Proper data isolation techniques.

Custom command optimization includes: 1) Efficient implementation patterns, 2) Proper chainability, 3) Optimized error handling, 4) Minimal overhead design, 5) Proper resource management. Focus on reusability without sacrificing performance.

Selector optimization includes: 1) Using specific selectors, 2) Proper attribute selection, 3) Minimizing DOM traversal, 4) Efficient element location strategies, 5) Proper caching of selectors.

Setup/teardown optimization includes: 1) Efficient state management, 2) Bulk operations, 3) Proper resource cleanup, 4) Minimized browser reloads, 5) Optimized data handling.

Network optimization includes: 1) Efficient request stubbing, 2) Proper response caching, 3) Minimized real network calls, 4) Optimized waiting strategies, 5) Efficient error handling.

Parallel execution optimization includes: 1) Proper test distribution, 2) Resource allocation strategies, 3) Minimized test dependencies, 4) Efficient state isolation, 5) Optimized reporting mechanisms.

Artifact optimization includes: 1) Selective artifact generation, 2) Efficient storage strategies, 3) Proper cleanup mechanisms, 4) Optimized file handling, 5) Compression techniques.

Browser optimization includes: 1) Efficient memory management, 2) Proper cache utilization, 3) Resource cleanup strategies, 4) Optimized DOM handling, 5) Minimized browser operations.

CI/CD optimization includes: 1) Efficient pipeline configuration, 2) Resource allocation strategies, 3) Proper caching mechanisms, 4) Optimized artifact handling, 5) Pipeline parallelization.

Reporting optimization includes: 1) Efficient data collection, 2) Optimized result processing, 3) Selective reporting strategies, 4) Proper resource utilization, 5) Streamlined output generation.

Advanced optimization includes: 1) Sophisticated test organization, 2) Complex performance analysis, 3) Advanced caching strategies, 4) Optimized resource management, 5) Advanced parallelization techniques.

Complex workflow optimization includes: 1) Advanced state management, 2) Sophisticated resource handling, 3) Optimized operation sequencing, 4) Efficient dependency management, 5) Advanced error handling.

Advanced memory optimization includes: 1) Sophisticated resource tracking, 2) Advanced cleanup strategies, 3) Memory profiling techniques, 4) Optimized data structure usage, 5) Advanced leak prevention.

Large-scale optimization includes: 1) Advanced test organization, 2) Sophisticated execution strategies, 3) Complex resource management, 4) Optimized reporting mechanisms, 5) Advanced parallelization approaches.

Framework optimization includes: 1) Advanced architecture patterns, 2) Sophisticated component design, 3) Optimized resource utilization, 4) Complex integration strategies, 5) Advanced performance tuning.

Large-scale data optimization includes: 1) Advanced data management patterns, 2) Sophisticated storage strategies, 3) Complex data synchronization, 4) Optimized cleanup mechanisms, 5) Advanced isolation techniques.

Advanced network optimization includes: 1) Sophisticated request handling, 2) Complex caching strategies, 3) Advanced request batching, 4) Optimized response processing, 5) Advanced error recovery.

Complex UI optimization includes: 1) Advanced event handling, 2) Sophisticated animation management, 3) Optimized DOM operations, 4) Complex state transitions, 5) Advanced rendering strategies.