Load and Performance Testing Questions and Answers for Viva

Answer-1: Load testing is a type of performance testing that verifies how a system behaves under expected load conditions, checking for response times, scalability, and reliability.

Answer-2: Performance testing ensures that a system performs as expected under load, meeting user expectations for speed, scalability, and stability, and detecting bottlenecks.

Answer-3: Load testing evaluates system behavior under normal or expected load, while stress testing pushes the system beyond its limits to identify failure points.

Answer-4: Key metrics include response time, throughput, latency, resource utilization (CPU, memory, disk), and error rates under different load conditions.

Answer-5: Common tools include Apache JMeter, LoadRunner, Gatling, NeoLoad, and BlazeMeter for simulating load and measuring system performance.

Answer-6: Scalability testing evaluates how well a system can handle increased loads, ensuring it can scale up (or down) efficiently with rising user demand.

Answer-7: A performance bottleneck is a point in the system where performance slows down due to resource limitations or inefficient processes. It can be identified through profiling and monitoring system components.

Answer-8: Throughput refers to the amount of data processed in a given time period, while response time refers to the time taken to process a request from the user.

Answer-9: User load can be simulated by creating virtual users using testing tools (e.g., JMeter, LoadRunner) that generate requests at specified rates to mimic real-world traffic.

Answer-10: Load capacity is determined by gradually increasing the load during testing until the system starts to degrade in performance or fail, identifying the maximum number of concurrent users it can support.

Answer-11: Stress testing is designed to push the system beyond normal load to find its breaking point. It is necessary to understand system limits and ensure robustness under extreme conditions.

Answer-12: Endurance testing involves running a system under a normal load for an extended period to identify memory leaks, performance degradation, or system failure over time.

Answer-13: Challenges include accurately simulating real-world traffic, dealing with network latency, identifying bottlenecks, and ensuring test environments mirror production setups.

Answer-14: Load testing for web applications involves simulating multiple user requests to the application?s URL, testing server capacity, and measuring response times under load.

Answer-15: A performance testing script automates the process of generating user load, defining test scenarios, and measuring system performance, ensuring consistency and repeatability.

Answer-16: Performance degradation is identified by monitoring system metrics, comparing the performance under different loads, and identifying increasing response times or resource utilization.

Answer-17: Latency refers to the time taken for a request to travel from the client to the server, while response time includes both latency and the time taken by the server to process the request.

Answer-18: A load test scenario defines a specific set of conditions under which the system is tested, such as the number of virtual users, frequency of requests, and types of operations.

Answer-19: Performance tests are made realistic by simulating real user behavior, using realistic data volumes, accounting for network conditions, and reflecting actual system architecture.

Answer-20: Server resource monitoring during load testing helps track system performance, identifying resource constraints (e.g., CPU, memory, bandwidth) that affect response times and scalability.

Answer-21: A performance baseline is a set of performance metrics (e.g., response time, throughput) recorded under normal conditions. It is used as a reference to compare performance under different loads.

Answer-22: The main goals include ensuring system stability, scalability, and responsiveness under expected and peak loads, identifying bottlenecks, and improving overall performance.

Answer-23: Horizontal scaling involves adding more machines or instances to handle increased load, while vertical scaling involves upgrading the capacity of existing machines (e.g., adding RAM or CPU).

Answer-24: Stress testing on a database system involves simulating heavy database transactions, large data reads and writes, and high concurrency to evaluate how the system handles database load under extreme conditions.

Answer-25: Cloud services like AWS, Azure, and Google Cloud can be used to scale test environments dynamically, simulate real-world traffic, and perform load testing on globally distributed applications.

Answer-26: Monitoring tools like New Relic, AppDynamics, and built-in system metrics can be used to track response times, server load, memory usage, and network performance during load testing.

Answer-27: A system failure during performance testing is handled by analyzing logs, identifying the failure point, adjusting test parameters, and retesting to prevent similar issues in production.

Answer-28: Throughput is the number of requests processed by the system per unit of time. It is measured by calculating the number of transactions handled during performance tests.

Answer-29: Scalability in cloud-based systems is ensured by using elastic cloud resources, auto-scaling, load balancing, and conducting performance tests to handle varying loads efficiently.

Answer-30: A virtual user is a simulated user created during load testing to mimic real user behavior and interactions with the application, generating the desired load.

Answer-31: Peak load refers to the maximum number of users or transactions the system is expected to handle during peak usage times. Load testing ensures the system can handle this load.

Answer-32: Traffic spikes are simulated by gradually increasing the number of virtual users or requests to test how the system handles sudden surges in traffic.

Answer-33: A bottleneck is any component of the system that limits performance, often caused by insufficient resources (e.g., CPU, memory, bandwidth) or inefficient algorithms.

Answer-34: Load testing for APIs involves simulating a high volume of API requests, monitoring API response times, throughput, and error rates, and ensuring the API can handle expected traffic levels.

Answer-35: Database performance significantly impacts overall system performance as inefficient queries, slow response times, or poor indexing can become bottlenecks affecting user experience.

Answer-36: System performance testing evaluates the entire system's performance, including hardware and network, while application performance testing focuses on how the software behaves under load.

Answer-37: Root causes of poor performance are identified by analyzing logs, monitoring server resources, profiling database queries, and reviewing error messages to pinpoint bottlenecks.

Answer-38: Performance tests can be automated using tools like JMeter, LoadRunner, or Gatling, which can simulate users, generate traffic, and collect performance metrics without manual intervention.

Answer-39: A performance test case defines specific conditions under which the system's performance is evaluated, such as the number of virtual users, types of transactions, and system behavior under load.

Answer-40: Resource utilization metrics (e.g., CPU, memory, disk I/O) help identify system strain under load, allowing testers to pinpoint and address resource-related performance issues.

Answer-41: Peak load testing verifies the system can handle the maximum expected load, while stress testing pushes the system beyond its limits to identify failure points under extreme conditions.

Answer-42: Repeatability is ensured by using consistent test environments, automating tests, and using controlled test data and scenarios to minimize variability.

Answer-43: Response time is calculated by measuring the time from when a request is made until the response is received, averaging this across multiple transactions under load.

Answer-44: Caching plays a role in performance testing by improving system response times, reducing server load, and optimizing resource utilization by storing frequently accessed data.

Answer-45: Load testing for mobile apps involves simulating multiple mobile devices or users accessing the app, testing server response times, and monitoring the mobile network performance.

Answer-46: Response time directly impacts user experience; if the response time is too high, users may experience delays, leading to frustration and dissatisfaction.

Answer-47: Results are analyzed by reviewing key metrics such as response time, throughput, error rates, and resource utilization, identifying bottlenecks and areas of improvement.

Answer-48: User load refers to the number of users interacting with the system, while system load refers to the total computational resources used (e.g., CPU, memory) to handle the requests.

Answer-49: Performance testing in the cloud involves using elastic resources to simulate load, leveraging cloud testing tools, and ensuring the application scales effectively under load.

Answer-50: Performance regression is dealt with by comparing current performance against baselines, identifying performance drops, and working with developers to resolve inefficiencies.