Scenario-Based K6 Interview Questions [2025]

Basic Scenario-Based Questions

1. What steps would you take in a scenario where a k6 test fails due to high error rates?

In a scenario where a k6 test fails due to high error rates, you analyze logs, isolate problematic endpoints, and optimize configurations. Steps include:

• Check http_req_failed metrics for error details.
• Review server logs for 5xx responses.
• Isolate failing scenarios with verbose mode.
• Adjust thresholds to pinpoint issues.
• Version fixes in Git for traceability.
• Retest in staging to validate.
• Monitor with Grafana for trends.

This ensures reliable debugging.

2. Why does a k6 test show unexpected latency spikes in a high-traffic scenario?

Latency spikes in high-traffic scenarios often stem from server bottlenecks or resource limits. Network issues or inefficient scripts can exacerbate problems. Analysis using http_req_duration metrics, server monitoring, and script optimization resolves spikes, aligning with progressive rollouts for stable performance.

3. When would you adjust VU counts in a scenario with fluctuating traffic?

Adjust VU counts when:

• Simulating peak user loads.
• Testing system recovery post-spike.
• Validating autoscaling triggers.
• Integrating with CI/CD pipelines.
• Versioning configurations in Git.
• Monitoring resource usage.
• Analyzing response times.

This models real-world traffic patterns.

4. Where do you store k6 test scripts for a team project scenario?

In a team project scenario, store k6 scripts in:

• Git repositories for version control.
• CI/CD pipeline configurations.
• Shared cloud storage for access.
• Docker images for consistency.
• Monitored artifact repositories.
• Team documentation wikis.
• Versioned branches for PRs.

This ensures collaboration.

5. Who handles k6 test failures in a production deployment scenario?

Performance engineers handle failures. They:

• Analyze error metrics in Grafana.
• Review script configurations.
• Test fixes in staging environments.
• Version changes in Git.
• Collaborate with developers.
• Monitor post-fix performance.
• Document root causes.

This resolves production issues.

6. Which executor would you choose in a scenario requiring sudden traffic spikes?

In a spike scenario, the ramping-vus executor is ideal, offering:

• Rapid VU increases for surge simulation.
• Customizable ramp-up durations.
• Integration with thresholds.
• Versioning in Git repositories.
• Monitoring spike impacts.
• Support for recovery tests.
• Scalability for bursts.

This tests system resilience.

7. How would you script a k6 test for a REST API in a load testing scenario?

Script a REST API test by:

• Using k6/http for GET/POST requests.
• Defining VUs in options.
• Adding checks for status codes.
• Setting thresholds for latency.
• Versioning scripts in Git.
• Monitoring with Grafana.
• Testing in CI/CD pipelines.

Example: javascript import http from 'k6/http'; export const options = { vus: 100, duration: '1m' }; export default function () { http.get('https://api.example.com/data'); } This validates API performance.

API Testing Scenarios

8. What do you do in a scenario where an API endpoint returns 429 errors during a k6 test?

When an API returns 429 errors, indicating rate limiting, you reduce request frequency, implement retries, and analyze rate limits. Steps include:

• Check http_req_failed for 429 counts.
• Add sleep() for pacing requests.
• Adjust VU counts to avoid throttling.
• Test retries in staging.
• Version changes in Git.
• Monitor API quotas.
• Consult API documentation.

This mitigates rate limits.

9. Why does a k6 test fail due to authentication issues in an API scenario?

Authentication failures occur due to invalid tokens or misconfigured headers. Expired credentials or incorrect OAuth scopes disrupt tests. Adding token refresh logic and validating headers in scripts, integrated with CI/CD, ensures robust authentication.

10. When would you parameterize API payloads in a k6 test scenario?

Parameterize payloads when:

• Simulating varied user inputs.
• Testing dynamic endpoints.
• Validating edge cases.
• Using CSV for data-driven tests.
• Versioning data in Git.
• Monitoring payload impacts.
• Ensuring realistic scenarios.

This enhances test coverage.

11. Where do you log API response errors in a k6 test scenario?

Log API response errors in:

• Console output with console.log.
• Grafana dashboards for metrics.
• JSON exports for analysis.
• CI/CD pipeline logs.
• Git-versioned error reports.
• Cloud test summaries.
• External observability tools.

This aids troubleshooting.

12. Who validates API performance in a k6 testing scenario?

QA engineers validate performance. They:

• Analyze http_req_duration metrics.
• Check threshold compliance.
• Test in staging environments.
• Integrate with CI/CD.
• Version results in Git.
• Monitor Grafana dashboards.
• Collaborate on optimizations.

This ensures reliability.

13. Which k6 module handles API authentication?

The k6/http module handles authentication by:

• Supporting OAuth headers.
• Adding token-based auth.
• Handling Basic Auth.
• Integrating with checks.
• Versioning in Git.
• Monitoring auth failures.
• Scaling for users.

This secures API tests.

14. How do you simulate a POST request in a k6 API test scenario?

Simulate a POST request by:

• Using http.post() with JSON payload.
• Setting headers for content type.
• Adding checks for response status.
• Testing in staging runs.
• Versioning scripts in Git.
• Monitoring response times.
• Parameterizing payloads.

Example: javascript import http from 'k6/http'; export default function () { http.post('https://api.example.com/submit', JSON.stringify({ data: 'test' })); } This tests POST endpoints.

15. What happens in a scenario where an API test exceeds memory limits?

Exceeding memory limits causes k6 test crashes or slowdowns. Large VU counts or unoptimized scripts contribute. Mitigation includes reducing VUs, optimizing loops, and monitoring memory usage in CI/CD pipelines for stable execution.

Stress and Spike Testing Scenarios

16. What actions do you take in a stress test scenario where the system crashes?

In a stress test crash, identify breaking points and optimize resources. Steps include:

• Analyze http_req_failed metrics.
• Check server CPU/memory usage.
• Reduce VU counts incrementally.
• Test fixes in staging.
• Version changes in Git.
• Monitor with Grafana.
• Scale server resources.

This pinpoints limits.

17. Why does a k6 spike test show inconsistent results?

Inconsistent spike test results stem from server autoscaling delays or network fluctuations. Unpredictable resource allocation causes variability. Using fixed seeds and monitoring with Grafana ensures repeatable outcomes, aligning with best practices for reliability.

18. When would you use a spike test in a k6 scenario?

Use a spike test when:

• Simulating sudden traffic surges.
• Testing autoscaling triggers.
• Validating recovery times.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring peak impacts.
• Analyzing failures.

This tests system resilience.

19. Where do you analyze spike test results?

Analyze spike test results in:

• Grafana for real-time metrics.
• InfluxDB for time-series data.
• JSON exports for reports.
• CI/CD pipeline logs.
• Git-versioned summaries.
• Cloud test dashboards.
• Observability platforms.

This provides insights.

20. Who configures spike tests in a k6 scenario?

Performance engineers configure spike tests. They:

• Select ramping-vus executor.
• Define peak VU counts.
• Set short durations.
• Test in staging environments.
• Version in Git.
• Monitor spike metrics.
• Adjust thresholds.

This simulates surges.

21. Which executor is best for a stress test scenario?

Ramping-vus is best for stress testing, offering:

• Gradual VU increases to max load.
• Support for threshold checks.
• Integration with scenarios.
• Versioning in Git.
• Monitoring breaking points.
• Scaling for stress.
• Analyzing recovery.

This identifies limits.

22. How do you handle a scenario where a stress test causes server downtime?

Handle server downtime by:

• Stopping the test immediately.
• Analyzing crash metrics.
• Checking server logs.
• Reducing VU counts.
• Testing fixes in staging.
• Versioning in Git.
• Monitoring recovery.

This prevents outages.

23. What are the steps to simulate a Black Friday traffic spike?

Simulating a Black Friday spike tests peak performance. Steps include defining a ramping-vus executor, setting high VUs, and monitoring for scalability issues.

Use ramping-vus with 1000 VUs. Set 10s ramp-up. Define thresholds for latency. Test in cloud environment. Monitor with Grafana. Version in Git. Analyze bottlenecks.

Browser Testing Scenarios

24. What do you do in a scenario where a k6 browser test fails due to timeouts?

In a browser test timeout scenario, adjust timeouts and optimize scripts. Steps include:

• Check browser_timeout metrics.
• Increase timeout in options.
• Reduce concurrent browser instances.
• Test in staging environments.
• Version changes in Git.
• Monitor Web Vitals.
• Debug with screenshots.

This resolves timeouts.

25. Why does a k6 browser test show high CLS in a scenario?

High Cumulative Layout Shift (CLS) indicates UI instability, often from dynamic content loading. Slow resource fetching or unoptimized scripts contribute. Capturing screenshots and analyzing Web Vitals, integrated with observability, optimizes frontend performance.

26. When would you use k6 browser testing in a scenario?

Use browser testing when:

• Validating frontend interactions.
• Measuring Web Vitals like LCP.
• Simulating user journeys.
• Integrating with API tests.
• Versioning in Git.
• Monitoring CLS/FID.
• Testing responsiveness.

This ensures UI reliability.

27. Where are browser test screenshots stored in a scenario?

Browser test screenshots are stored in:

• Local directories during runs.
• Cloud storage for cloud tests.
• Git repositories for versioning.
• CI/CD artifacts.
• Monitored debug folders.
• External observability tools.
• Test result archives.

This aids debugging.

28. Who runs k6 browser tests in a scenario?

Frontend developers run browser tests. They:

• Script click/navigation flows.
• Capture Web Vitals metrics.
• Test in staging browsers.
• Integrate with load tests.
• Version scripts in Git.
• Monitor UI performance.
• Optimize layouts.

This validates user experience.

29. Which module enables browser testing in a k6 scenario?

The experimental.browser module enables browser testing by:

• Launching Chromium instances.
• Supporting Playwright-like APIs.
• Capturing screenshots.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring Web Vitals.
• Scaling for users.

This tests frontend performance.

30. How do you simulate a user login in a k6 browser test scenario?

Simulate a user login by:

• Using browser module to open pages.
• Entering credentials via inputs.
• Clicking login buttons.
• Checking response status.
• Versioning scripts in Git.
• Monitoring session metrics.
• Testing in staging.

Example: javascript import { browser } from 'k6/experimental/browser'; export default async function () { const page = browser.newPage(); await page.goto('https://example.com/login'); await page.locator('#login').click(); } This validates login flows.

31. What happens in a scenario where browser tests consume excessive CPU?

Excessive CPU in browser tests results from too many browser instances or unoptimized scripts. Reducing concurrent browsers, optimizing selectors, and monitoring with Grafana in staging environments mitigates resource strain.

CI/CD Integration Scenarios

32. What do you do in a scenario where a k6 test fails in a CI/CD pipeline?

In a CI/CD pipeline failure, analyze logs and isolate issues. Steps include:

• Check pipeline logs for errors.
• Review threshold violations.
• Run locally to replicate.
• Fix script configurations.
• Version changes in Git.
• Retest in pipeline.
• Monitor with dashboards.

This ensures pipeline stability.

33. Why does a k6 test timeout in a CI/CD scenario?

Timeouts in CI/CD occur due to resource constraints or slow server responses. Long-running scenarios or insufficient runners cause delays. Adjusting durations and scaling runners in GitHub Actions resolves issues.

34. When would you run k6 tests in a CI/CD pipeline?

Run k6 tests in CI/CD when:

• Validating pull requests.
• Checking pre-deployment gates.
• Testing nightly builds.
• Integrating with GitOps.
• Versioning in Git.
• Monitoring performance.
• Enforcing SLOs.

This prevents regressions.

35. Where are k6 test results stored in a CI/CD scenario?

Test results are stored in:

• Pipeline artifacts for access.
• Grafana dashboards for metrics.
• JSON files for analysis.
• Git repositories for versioning.
• Cloud storage for archives.
• CI/CD logs.
• Observability platforms.

This ensures traceability.

36. Who configures k6 tests in a CI/CD pipeline?

DevOps engineers configure tests. They:

• Define pipeline YAML files.
• Set up k6 runners.
• Integrate thresholds.
• Test in staging pipelines.
• Version in Git.
• Monitor results.
• Handle failures.

This automates validation.

37. Which CI tool integrates seamlessly with k6?

GitHub Actions integrates seamlessly with k6 by:

• Using actions/setup-k6.
• Supporting matrix testing.
• Publishing test artifacts.
• Versioning in Git.
• Monitoring with badges.
• Scaling runners.
• Alerting on failures.

This simplifies automation.

38. How do you handle a scenario where k6 tests slow down a CI/CD pipeline?

Handle slow tests by:

• Reducing VU counts.
• Optimizing script logic.
• Running in parallel jobs.
• Testing in staging pipelines.
• Versioning in Git.
• Monitoring pipeline times.
• Adjusting durations.

This improves pipeline efficiency.

39. What are the steps to integrate k6 with Jenkins in a CI/CD scenario?

Integrating k6 with Jenkins ensures automated performance checks. Steps include setting up runners, defining pipelines, and publishing results for continuous testing.

Install k6 on Jenkins agents. Create Jenkinsfile with k6 stage. Set thresholds for gates. Publish JUnit reports. Monitor with plugins. Version in Git.

Threshold and Metrics Scenarios

40. What do you do in a scenario where a k6 threshold fails for p95 latency?

In a p95 latency failure, analyze metrics and optimize endpoints. Steps include:

• Check http_req_duration[p95].
• Identify slow endpoints.
• Optimize server resources.
• Adjust VU counts.
• Version fixes in Git.
• Retest in staging.
• Monitor with Grafana.

This ensures SLA compliance.

41. Why does a k6 test fail error rate thresholds in a scenario?

Error rate threshold failures occur due to server errors or script issues. High 4xx/5xx responses or misconfigured checks cause breaches. Analyzing http_req_failed and fixing endpoints ensures reliability in performance scenarios.

42. When would you adjust thresholds in a k6 test scenario?

Adjust thresholds when:

• Aligning with new SLAs.
• Testing new endpoints.
• Handling temporary spikes.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring pass rates.
• Validating performance.

This ensures accuracy.

43. Where are threshold results analyzed in a k6 scenario?

Threshold results are analyzed in:

• Grafana for real-time insights.
• JSON exports for reports.
• CI/CD pipeline logs.
• Git-versioned summaries.
• Cloud test dashboards.
• InfluxDB for trends.
• Observability tools.

This drives optimizations.

44. Who defines k6 thresholds in a performance scenario?

Performance engineers define thresholds. They:

• Base on SLA requirements.
• Test expressions locally.
• Integrate with scenarios.
• Monitor pass/fail rates.
• Version in Git.
• Collaborate with teams.
• Adjust for realism.

This sets quality gates.

45. Which metric is critical in a high-latency test scenario?

http_req_duration is critical, offering:

• Percentile tracking like p95.
• Threshold integration.
• Real-time monitoring.
• Versioning in Git.
• Grafana visualization.
• Scaling for analysis.
• Alerting on spikes.

This measures latency.

46. How do you handle a scenario where thresholds are too strict?

Handle strict thresholds by:

• Reviewing SLA alignment.
• Adjusting p95 limits.
• Testing relaxed thresholds.
• Monitoring pass rates.
• Versioning in Git.
• Consulting stakeholders.
• Retesting in CI/CD.

This balances realism.

47. What happens in a scenario where custom metrics fail to collect?

Custom metric failures result from script errors or misconfigured exports. Incorrect tagging or InfluxDB issues disrupt collection. Debugging scripts and validating outputs in PlatformOps environments ensures accurate metric tracking.

WebSocket and Real-Time Scenarios

48. What do you do in a scenario where a WebSocket test fails to connect?

In a WebSocket connection failure, check configurations and server status. Steps include:

• Verify ws.connect() parameters.
• Check server endpoint status.
• Enable verbose logging.
• Test in staging environments.
• Version fixes in Git.
• Monitor connection metrics.
• Debug with curl.

This resolves connectivity issues.

49. Why does a WebSocket test show message drops in a scenario?

Message drops occur due to server overload or network instability. High VU counts or weak connections cause issues. Monitoring ws_msg_received and optimizing server capacity ensures reliable real-time communication.

50. When would you use k6 for WebSocket testing?

Use WebSocket testing when:

• Validating chat applications.
• Simulating real-time streams.
• Testing message throughput.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring latency.
• Handling disconnections.

This ensures real-time performance.

51. Where are WebSocket metrics logged in a scenario?

WebSocket metrics are logged in:

• Console output for debugging.
• Grafana for visualization.
• JSON exports for analysis.
• CI/CD pipeline logs.
• Git-versioned reports.
• Cloud test summaries.
• Observability platforms.

This aids analysis.

52. Who implements WebSocket tests in a k6 scenario?

Full-stack developers implement WebSocket tests. They:

• Script ws.connect() logic.
• Simulate message exchanges.
• Test error handling.
• Integrate with thresholds.
• Version in Git.
• Monitor metrics.
• Optimize connections.

This validates real-time apps.

53. Which module supports WebSocket testing?

k6/ws supports WebSocket testing by:

• Establishing connections.
• Sending/receiving messages.
• Handling events.
• Integrating with checks.
• Versioning in Git.
• Monitoring latency.
• Scaling connections.

This tests real-time features.

54. How do you simulate a chat application in a WebSocket scenario?

Simulate a chat application by:

• Using k6/ws for connections.
• Sending periodic messages.
• Checking message receipt.
• Testing with multiple VUs.
• Versioning in Git.
• Monitoring throughput.
• Handling disconnections.

Example: javascript import ws from 'k6/ws'; export default function () { ws.connect('ws://chat.example.com', socket => { socket.send('Hello'); }); } This validates chat performance.

55. What happens in a scenario where WebSocket tests overload the server?

Server overload in WebSocket tests causes disconnections or latency spikes. High VU counts or unoptimized scripts contribute. Reducing VUs and monitoring ws_msg_sent in canary tests mitigates issues.

Soak and Endurance Testing Scenarios

56. What do you do in a soak test scenario where memory leaks are detected?

In a soak test with memory leaks, analyze metrics and optimize resources. Steps include:

• Monitor vus_active metrics.
• Check server memory usage.
• Reduce test duration initially.
• Test fixes in staging.
• Version changes in Git.
• Monitor with Grafana.
• Optimize garbage collection.

This identifies leaks.

57. Why does a soak test fail after long durations?

Soak test failures after long durations result from resource exhaustion or connection leaks. Database bottlenecks or unclosed connections cause degradation. Monitoring trends and optimizing resources ensures endurance in long-running scenarios.

58. When would you use soak testing in a k6 scenario?

Use soak testing when:

• Validating long-term stability.
• Detecting memory leaks.
• Testing database endurance.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring trends.
• Setting long durations.

This uncovers gradual issues.

59. Where are soak test metrics analyzed?

Soak test metrics are analyzed in:

• Grafana for trend visualization.
• InfluxDB for time-series.
• JSON exports for reports.
• CI/CD pipeline logs.
• Git-versioned summaries.
• Cloud test dashboards.
• Observability platforms.

This reveals patterns.

60. Who runs soak tests in a k6 scenario?

QA engineers run soak tests. They:

• Configure extended durations.
• Monitor resource usage.
• Analyze performance trends.
• Test in staging environments.
• Version in Git.
• Integrate with alerts.
• Report findings.

This ensures stability.

61. Which executor is best for soak testing?

Constant-vus is best for soak testing by:

• Maintaining steady VU counts.
• Running for long durations.
• Testing system endurance.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring stability.
• Analyzing trends.

This tests longevity.

62. How do you handle a scenario where a soak test reveals database bottlenecks?

Handle database bottlenecks by:

• Analyzing query performance.
• Checking connection pools.
• Optimizing indexes.
• Testing in staging.
• Versioning fixes in Git.
• Monitoring database metrics.
• Reducing VU load.

This improves performance.

63. What are the steps to simulate a week-long user load in a soak test?

Simulating a week-long load tests endurance. Steps include configuring constant-vus, monitoring resources, and analyzing trends for stability insights.

Use constant-vus with 50 VUs. Set duration to 7d. Define latency thresholds. Monitor memory usage. Version in Git. Analyze with Grafana.

Cloud and Distributed Testing Scenarios

64. What do you do in a scenario where a k6 cloud test fails to scale?

In a cloud test scaling failure, check configurations and limits. Steps include:

• Verify VU allocation settings.
• Check cloud account quotas.
• Reduce concurrent scenarios.
• Test in smaller regions.
• Version changes in Git.
• Monitor cloud dashboards.
• Contact k6 support.

This ensures scalability.

65. Why does a k6 cloud test show inconsistent results?

Inconsistent cloud test results stem from network latency or regional differences. Unbalanced VU distribution causes variability. Using fixed regions and monitoring with Grafana in cloud setups ensures consistency.

66. When would you use k6 cloud for testing?

Use k6 cloud when:

• Simulating global traffic.
• Scaling beyond local limits.
• Collaborating on results.
• Integrating with Grafana.
• Versioning in Git.
• Monitoring dashboards.
• Running distributed loads.

This handles large-scale tests.

67. Where are k6 cloud test results stored?

Cloud test results are stored in:

• k6 cloud dashboards.
• Exported JSON files.
• Grafana for visualization.
• Git repositories for versioning.
• CI/CD artifacts.
• Cloud storage.
• Observability platforms.

This enables access.

68. Who manages k6 cloud tests in a scenario?

DevOps engineers manage cloud tests. They:

• Configure cloud scenarios.
• Monitor distributed metrics.
• Test in staging regions.
• Integrate with CI/CD.
• Version in Git.
• Analyze results.
• Optimize scaling.

This ensures global testing.

69. Which feature supports k6 cloud scalability?

Distributed execution supports scalability by:

• Running VUs across regions.
• Balancing load dynamically.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring in dashboards.
• Scaling millions of VUs.
• Handling bursts.

This enables large tests.

70. How do you handle a scenario where k6 cloud costs exceed budget?

Handle budget overruns by:

• Reducing VU counts.
• Optimizing test durations.
• Using local runs for debugging.
• Monitoring usage reports.
• Versioning in Git.
• Adjusting cloud regions.
• Setting budget alerts.

This controls costs.

71. What are the steps to set up a k6 cloud test?

Setting up a k6 cloud test enables global load testing. Steps include account setup, script upload, and configuration for distributed execution.

Create k6 cloud account. Upload scripts via CLI. Configure scenarios and VUs. Schedule tests. Monitor dashboards. Version in Git.

Error Handling Scenarios

72. What do you do in a scenario where k6 scripts throw JavaScript errors?

In a JavaScript error scenario, debug scripts and validate logic. Steps include:

• Enable verbose logging.
• Check console.log outputs.
• Isolate faulty code blocks.
• Test locally to replicate.
• Version fixes in Git.
• Monitor error rates.
• Use linters for syntax.

This resolves script issues.

73. Why does a k6 test fail due to async issues in a scenario?

Async issues cause failures from unhandled promises or race conditions. Incorrect await usage disrupts flow. Adding try-catch and validating async logic ensures robust scripts, aligned with network testing best practices.

74. When would you add error handling in a k6 test scenario?

Add error handling when:

• Testing unreliable APIs.
• Simulating network failures.
• Validating edge cases.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring errors.
• Ensuring robustness.

This improves reliability.

75. Where are k6 error logs stored?

Error logs are stored in:

• Console output during runs.
• CI/CD pipeline logs.
• JSON exports for analysis.
• Git-versioned reports.
• Grafana dashboards.
• Cloud test summaries.
• Observability tools.

This aids debugging.

76. Who handles k6 error debugging?

Performance engineers handle debugging. They:

• Analyze error metrics.
• Review script logic.
• Test isolated cases.
• Integrate with logs.
• Version fixes in Git.
• Monitor resolutions.
• Document issues.

This resolves errors.

77. Which practice improves k6 error handling?

Try-catch blocks improve error handling by:

• Catching runtime errors.
• Logging error details.
• Integrating with metrics.
• Versioning in Git.
• Monitoring error rates.
• Supporting retries.
• Ensuring robustness.

This prevents crashes.

78. How do you simulate network failures in a k6 test scenario?

Simulate network failures by:

• Using fail() for error injection.
• Setting low timeout values.
• Testing with unstable URLs.
• Monitoring error metrics.
• Versioning in Git.
• Integrating with checks.
• Analyzing recovery.

This tests resilience.

79. What happens in a scenario where k6 tests encounter rate limit errors?

Rate limit errors disrupt tests, causing 429 responses. High VU counts or missing pacing contribute. Adding sleep() and reducing VUs in API versioning scenarios ensures compliance with limits.

Real-World Application Scenarios

80. What do you do in a scenario where a k6 test for a microservice fails?

In a microservice test failure, isolate the service and analyze metrics. Steps include:

• Check http_req_failed for errors.
• Review service logs.
• Test individual endpoints.
• Adjust VU counts.
• Version fixes in Git.
• Monitor with Grafana.
• Collaborate with developers.

This pinpoints issues.

81. Why does a k6 test for a monolithic app show inconsistent performance?

Inconsistent performance in monolithic apps results from resource contention or database bottlenecks. Unoptimized queries cause variability. Analyzing metrics and optimizing backend logic ensures stable performance across test runs.

82. When would you use k6 to test a payment gateway?

Use k6 for payment gateways when:

• Simulating transaction loads.
• Validating response times.
• Testing error handling.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring metrics.
• Ensuring reliability.

This validates critical flows.

83. Where are payment gateway test results analyzed?

Payment gateway results are analyzed in:

• Grafana for transaction metrics.
• JSON exports for reports.
• CI/CD pipeline logs.
• Git-versioned summaries.
• Cloud test dashboards.
• InfluxDB for trends.
• Observability tools.

This ensures accuracy.

84. Who tests payment gateways with k6?

QA engineers test payment gateways. They:

• Script transaction flows.
• Validate response times.
• Test error scenarios.
• Integrate with thresholds.
• Version in Git.
• Monitor metrics.
• Collaborate with devs.

This ensures reliability.

85. Which executor simulates a payment spike?

Ramping-vus simulates payment spikes by:

• Increasing VUs rapidly.
• Testing transaction surges.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring peak metrics.
• Scaling for bursts.
• Analyzing recovery.

This tests high loads.

86. How do you test a streaming service with k6?

Test a streaming service by:

• Using k6/ws for connections.
• Simulating stream requests.
• Checking buffering times.
• Testing with multiple VUs.
• Versioning in Git.
• Monitoring latency.
• Handling drops.

This validates streaming performance.

87. What happens in a scenario where a streaming test fails due to buffering?

Buffering failures in streaming tests result from bandwidth issues or server delays. High latency disrupts streams. Optimizing CDN settings and monitoring ws_msg_received in GitOps workflows resolves issues.

Advanced and Edge Case Scenarios

88. What do you do in a scenario where a k6 test runs out of VUs?

In a VU exhaustion scenario, adjust configurations and optimize scripts. Steps include:

• Check vus_max settings.
• Reduce concurrent scenarios.
• Optimize script iterations.
• Test in smaller batches.
• Version changes in Git.
• Monitor VU allocation.
• Scale cloud resources.

This prevents exhaustion.

89. Why does a k6 test fail in a low-bandwidth scenario?

Low-bandwidth failures occur due to network throttling or timeouts. Slow connections disrupt requests. Adding retries and adjusting timeouts ensures stability in constrained environments.

90. When would you use custom metrics in a k6 scenario?

Use custom metrics when:

• Tracking business KPIs.
• Analyzing user journeys.
• Validating custom logic.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring in Grafana.
• Troubleshooting issues.

This captures unique data.

91. Where are custom metrics stored in a k6 scenario?

Custom metrics are stored in:

• InfluxDB for time-series.
• Grafana for visualization.
• JSON exports for analysis.
• Git-versioned reports.
• CI/CD pipeline logs.
• Cloud test summaries.
• Observability platforms.

This enables tracking.

92. Who defines custom metrics in a k6 scenario?

Performance engineers define custom metrics. They:

• Identify business KPIs.
• Implement with add().
• Test in staging runs.
• Integrate with thresholds.
• Version in Git.
• Monitor in Grafana.
• Optimize logic.

This captures domain data.

93. Which practice handles edge cases in k6 tests?

Data-driven testing handles edge cases by:

• Using CSV for inputs.
• Simulating error conditions.
• Integrating with checks.
• Versioning in Git.
• Monitoring edge metrics.
• Scaling for scenarios.
• Validating outliers.

This ensures robustness.

94. How do you simulate a DDoS attack in a k6 test scenario?

Simulate a DDoS attack by:

• Using ramping-vus with high VUs.
• Sending rapid requests.
• Testing rate limits.
• Monitoring server crashes.
• Versioning in Git.
• Analyzing thresholds.
• Scaling cloud tests.

This tests defenses.

95. What are the steps to test a multi-region API with k6 cloud?

Testing a multi-region API ensures global performance. Steps include configuring cloud regions, running distributed tests, and analyzing latency for consistency.

Configure k6 cloud regions. Upload API scripts. Set VUs per region. Define latency thresholds. Monitor dashboards. Version in Git.

96. Why does a k6 test fail in a multi-region scenario?

Multi-region test failures occur due to latency variations or misconfigured regions. Unbalanced VU distribution causes inconsistencies. Selecting stable regions and monitoring with Grafana ensures reliable results.

97. When to use k6 for multi-tenant testing?

Use k6 for multi-tenant testing when:

• Simulating tenant loads.
• Validating isolation.
• Testing quota limits.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring metrics.
• Ensuring fairness.

This validates tenancy.

98. Where are multi-tenant test results stored?

Multi-tenant results are stored in:

• Grafana for tenant metrics.
• JSON exports for analysis.
• CI/CD pipeline logs.
• Git-versioned reports.
• Cloud test dashboards.
• InfluxDB for trends.
• Observability tools.

This separates tenant data.

99. Who runs multi-tenant k6 tests?

Platform engineers run multi-tenant tests. They:

• Script tenant scenarios.
• Validate quota enforcement.
• Test in staging environments.
• Integrate with CI/CD.
• Version in Git.
• Monitor tenant metrics.
• Optimize isolation.

This ensures fairness.

100. Which executor supports multi-tenant load testing?

Per-vu-iterations supports multi-tenant testing by:

• Assigning fixed iterations per VU.
• Simulating tenant loads.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring tenant metrics.
• Scaling for tenants.
• Ensuring isolation.

This balances loads.

101. How do you handle a scenario where k6 tests reveal autoscaling issues?

Handle autoscaling issues by:

• Analyzing http_req_duration spikes.
• Checking Kubernetes HPA logs.
• Adjusting scaling thresholds.
• Testing in staging clusters.
• Versioning in Git.
• Monitoring with Grafana.
• Optimizing triggers.

This improves scalability.

102. What are the steps to validate a k6 test in a production-like environment?

Validating k6 tests in production-like environments ensures realism. Steps include setting up staging, running tests, and analyzing results for deployment readiness.

Replicate production configs. Run k6 with realistic VUs. Set thresholds for SLAs. Monitor with Grafana. Version in Git. Analyze results.

103. Why does a k6 test fail in a production-like scenario?

Failures in production-like scenarios result from misaligned configurations or resource limits. Unoptimized endpoints or missing indexes cause issues. Validating configs and monitoring with Grafana ensures production readiness.