Load Testing in Cloud Questions and Answers for Viva

Answer-1: Load testing evaluates how a system performs under expected user load to identify performance bottlenecks.

Answer-2: It ensures that cloud-hosted applications can scale and perform reliably under varying loads.

Answer-3: Popular tools include Apache JMeter,Gatling,Locust,BlazeMeter,and Azure Load Testing.

Answer-4: Load testing checks expected load capacity,while stress testing evaluates performance beyond maximum thresholds.

Answer-5: Yes,serverless functions like AWS Lambda or Azure Functions can be load tested to ensure scaling and cost efficiency.

Answer-6: Auto-scaling automatically adds or removes resources based on real-time demand to maintain performance levels.

Answer-7: JMeter can be run on distributed cloud instances to simulate concurrent users across geographies.

Answer-8: Key metrics include response time,throughput,latency,error rate,CPU/memory usage,and network I/O.

Answer-9: You pay only for the compute time/resources used during testing,eliminating the need for expensive on-prem hardware.

Answer-10: A virtual user simulates real users interacting with the system to evaluate its performance under load.

Answer-11: Load levels are determined based on peak traffic,historical usage data,and business goals.

Answer-12: Ramp-up time is the duration over which virtual users are gradually introduced to the system.

Answer-13: BlazeMeter is a cloud-based performance testing tool compatible with JMeter,designed for large-scale load testing.

Answer-14: Azure Load Testing allows you to simulate traffic from multiple regions and provides built-in performance insights.

Answer-15: It monitors resource metrics like CPU,memory,and latency during load testing in AWS environments.

Answer-16: Test scripts define user behavior,requests,and interactions used to simulate real-world usage.

Answer-17: A load test plan outlines objectives,test scenarios,user loads,tools,success criteria,and timelines.

Answer-18: You configure the testing tool to simulate concurrent users or API calls to replicate real peak conditions.

Answer-19: Throughput is the number of requests processed per unit time,reflecting application capacity.

Answer-20: While not its primary goal,it may uncover issues like insecure session handling under heavy load.

Answer-21: Latency is the time it takes for a request to travel from the client to the server and back.

Answer-22: You compare measured performance metrics against agreed service-level objectives under load conditions.

Answer-23: You must test individual services and their interactions,often requiring container orchestration for accuracy.

Answer-24: It refers to slower system behavior or errors as user load increases,often due to bottlenecks.

Answer-25: Containers allow consistent environments and can be scaled up/down to simulate realistic user traffic.

Answer-26: You generate or replicate realistic datasets to simulate actual user behavior and workflows.

Answer-27: Yes,many CI/CD tools integrate with load testing frameworks for automated performance validation.

Answer-28: They are parts of a system (e.g.,database,CPU) that limit overall throughput and response time.

Answer-29: Client-side tests simulate UI interactions; server-side tests target APIs and backend services directly.

Answer-30: It means 90% of the requests completed within that time,providing a better view of user experience.

Answer-31: The percentage of failed requests out of total requests,used to assess system stability.

Answer-32: Use cloud test agents in different regions to simulate real-world,geo-distributed usage.

Answer-33: Use dashboards,logs,and metrics to interpret performance,locate bottlenecks,and validate SLAs.

Answer-34: Soak testing involves running the system under load for an extended period to identify memory leaks or degradation.

Answer-35: Spike testing involves suddenly increasing the load to test how the system handles abrupt traffic surges.

Answer-36: It?s the distribution of incoming traffic across multiple servers to ensure no single server is overwhelmed.

Answer-37: Caching can improve performance by reducing load on the backend,so tests should account for cache hits/misses.

Answer-38: Yes,CDNs can offload traffic from origin servers,skewing backend performance metrics if not isolated.

Answer-39: Simulate queries,inserts,and updates to test database responsiveness,locking,and resource contention.

Answer-40: Horizontal adds more instances; vertical increases resources of a single instance.

Answer-41: It prevents impact on production and allows full control over test conditions and resources.

Answer-42: Yes,heavy load tests consume compute,network,and storage resources,affecting your billing.

Answer-43: It uses simulated traffic and behavior patterns to evaluate performance without relying on real users.

Answer-44: Configure load test scripts to make concurrent API calls with realistic payloads and headers.

Answer-45: Service Level Agreements define the acceptable performance standards for uptime and response times.

Answer-46: It helps ensure systems can handle traffic rerouting or failover scenarios under stress.

Answer-47: The number of simultaneous users or threads sending requests during a load test.

Answer-48: Scalability,geographic distribution,pay-as-you-go pricing,and ease of setup.

Answer-49: Yes,to analyze patterns,predict thresholds,or identify anomalies in performance data.

Answer-50: Load tests can be triggered automatically in pipelines to ensure performance doesn?t degrade with new changes.