Scenario-Based Dynatrace Interview Questions [2025]

Dynatrace Core Scenarios

1. What would you do if Dynatrace fails to monitor a Kubernetes cluster?

In a scenario where Dynatrace fails to monitor a Kubernetes cluster, verify OneAgent deployment via Helm charts. Check RBAC permissions, network connectivity, and pod logs for errors. Test in a staging environment, integrate with Prometheus for additional metrics, and use analytics to identify gaps, ensuring comprehensive cluster observability in DevOps.

2. Why might Dynatrace miss critical application metrics?

• Incomplete OneAgent instrumentation.
• Misconfigured Kubernetes integrations.
• Network latency affecting data collection.
• Insufficient RBAC permissions.
• Limited Prometheus metric scraping.
• Incorrect Davis AI thresholds.
• Compliance restrictions on data access.

3. When would you deploy Dynatrace in a multi-cloud environment?

Deploy Dynatrace in a multi-cloud environment when scaling applications across AWS, Azure, and GCP require unified observability. In a scenario with microservices, configure OneAgent for services, ActiveGate for cloud APIs, and Davis AI for anomaly detection, ensuring compliance and real-time monitoring in DevOps.

4. Where would you check if Dynatrace data collection is incomplete?

• OneAgent logs for instrumentation errors.
• Kubernetes pod configurations for RBAC.
• ActiveGate for cloud API connectivity.
• Prometheus endpoints for metric gaps.
• CI/CD pipeline logs for integration issues.
• Davis AI settings for threshold errors.
• Cluster dashboards for missing data.

5. Who would you involve to resolve Dynatrace deployment issues?

In a scenario where Dynatrace deployment fails, involve SRE engineers to troubleshoot OneAgent issues, DevOps teams for CI/CD integration, and cloud architects for multi-cloud configurations. Test in staging, collaborate for RBAC fixes, and ensure compliance, restoring observability in DevOps.

6. Which Dynatrace tools would you use to troubleshoot a monitoring gap?

• OneAgent for instrumentation diagnostics.
• Davis AI for anomaly correlation.
• ActiveGate for cloud API checks.
• API for custom metric queries.
• Dashboards for visualization gaps.
• Prometheus extensions for metrics.
• Analytics for data trend analysis.

7. How would you configure Dynatrace for a new Kubernetes cluster?

In a scenario where a new Kubernetes cluster is deployed, install OneAgent using Helm charts for automatic instrumentation. Configure RBAC for permissions, integrate with Prometheus for metrics, and set up Davis AI for anomaly detection. Test in staging and use dashboards for visualization, ensuring real-time observability in DevOps.

Validate configurations for compliance.

Learn more about Kubernetes automation.

8. What would you do if Dynatrace OneAgent causes performance overhead?

If OneAgent causes performance overhead in a Kubernetes cluster, reduce its resource allocation in Helm configurations. Monitor CPU/memory usage, test in staging, and adjust sampling rates. Use analytics to balance performance and observability, ensuring efficient DevOps monitoring.

9. Why might Dynatrace fail to detect CI/CD pipeline failures?

• Misconfigured Jenkins integrations.
• Incomplete OneAgent deployment in CI/CD.
• Network issues delaying data collection.
• Incorrect Davis AI alert thresholds.
• Limited API access to pipeline metrics.
• Compliance restrictions on logs.
• Insufficient dashboard configurations.

10. When would you use Dynatrace to monitor a CI/CD pipeline?

Use Dynatrace to monitor a CI/CD pipeline when build failures or delays impact deployments. In a scenario with Jenkins, configure OneAgent for pipeline instrumentation, Davis AI for anomaly detection, and PagerDuty for alerts, ensuring real-time DevOps reliability.

11. Where would you look if Dynatrace misses pipeline metrics?

• Jenkins plugin logs for integration errors.
• OneAgent configurations for pipeline data.
• API endpoints for metric collection.
• Kubernetes logs for deployment issues.
• Davis AI settings for threshold gaps.
• Dashboards for missing visualizations.
• Analytics for pipeline data trends.

12. Who would you consult for Dynatrace CI/CD integration issues?

For CI/CD integration issues, consult DevOps engineers for Jenkins configurations, SREs for Dynatrace OneAgent setup, and compliance officers for audit logs. Test integrations in staging, align with Kubernetes monitoring, and ensure real-time observability in DevOps.

13. Which Dynatrace components would you use for pipeline monitoring?

• OneAgent for pipeline instrumentation.
• Davis AI for anomaly detection.
• API for Jenkins metric export.
• Dashboards for pipeline visualization.
• Analytics for performance trends.
• Prometheus extensions for metrics.
• PagerDuty for alert integration.

14. How would you resolve a Dynatrace alert overload in a pipeline?

In a scenario with excessive Dynatrace alerts from a CI/CD pipeline, refine Davis AI thresholds to prioritize critical issues. Integrate with PagerDuty for escalation, test in staging, and use analytics to reduce false positives, ensuring efficient alert management in DevOps.

Review alert rules for optimization.

Explore pipeline standardization for better monitoring.

Incident Response Scenarios

15. What would you do if Dynatrace fails to alert on a Kubernetes outage?

If Dynatrace fails to alert on a Kubernetes outage, verify Davis AI rules and OneAgent deployment. Check RBAC permissions, test in staging, and integrate with PagerDuty for notifications. Use analytics to identify gaps, ensuring reliable alerting in DevOps.

16. Why might Dynatrace generate excessive false alerts?

• Overly sensitive Davis AI thresholds.
• Incomplete Kubernetes metric coverage.
• Misconfigured PagerDuty integrations.
• Network delays in data collection.
• Improperly tuned anomaly detection.
• Limited compliance log filters.
• Inadequate testing in staging.

17. When would you escalate a Dynatrace-detected incident?

Escalate a Dynatrace-detected incident when Davis AI identifies a critical Kubernetes failure impacting production. Configure PagerDuty for immediate notifications, use dashboards for context, and collaborate with SREs for resolution, ensuring minimal downtime in DevOps.

18. Where would you check for delayed Dynatrace alerts?

• OneAgent logs for collection delays.
• PagerDuty integration for routing issues.
• Kubernetes events for metric gaps.
• Davis AI settings for alert triggers.
• Network logs for connectivity issues.
• Cluster dashboards for visualization.
• Analytics for alert performance trends.

19. Who would you involve in resolving Dynatrace alert issues?

Involve SREs for Davis AI tuning, DevOps engineers for Kubernetes integration, and incident responders for PagerDuty configurations. Test alert workflows in staging and use analytics to optimize, ensuring effective incident response in DevOps.

20. Which Dynatrace tools would you use for incident response?

• Davis AI for root cause analysis.
• PagerDuty for alert escalation.
• Dashboards for incident visualization.
• API for custom incident queries.
• Analytics for incident trends.
• OneAgent for real-time data.
• Compliance logs for auditing.

21. How would you handle a Dynatrace alert storm in production?

In a production alert storm scenario, adjust Davis AI thresholds to filter low-priority alerts. Integrate with PagerDuty for prioritized escalation, test in staging, and use dashboards to visualize trends, ensuring efficient incident management in DevOps.

Document alert resolutions for compliance.

Learn about event-driven pipelines for alert optimization.

22. What would you do if Dynatrace misses a critical application issue?

If Dynatrace misses a critical application issue, verify OneAgent instrumentation and Kubernetes service configurations. Test in staging, integrate with OpenTelemetry for additional traces, and use analytics to identify gaps, ensuring comprehensive monitoring in DevOps.

23. Why might Dynatrace fail to correlate incident data?

• Incomplete OneAgent data collection.
• Misconfigured Kubernetes integrations.
• Limited Prometheus metric scraping.
• Incorrect Davis AI correlation rules.
• Network issues affecting data sync.
• Compliance restrictions on logs.
• Insufficient dashboard configurations.

24. When would you use Dynatrace for root cause analysis?

Use Dynatrace for root cause analysis when a Kubernetes application fails in production. Configure Davis AI to correlate metrics, logs, and traces, integrate with PagerDuty for alerts, and use dashboards for insights, ensuring rapid DevOps resolution.

25. Where would you look for Dynatrace correlation failures?

• OneAgent logs for data gaps.
• Kubernetes events for metric issues.
• Davis AI rules for correlation errors.
• Prometheus endpoints for missing data.
• CI/CD logs for pipeline context.
• Dashboards for visualization issues.
• Analytics for correlation trends.

26. Who would you consult for Dynatrace incident correlation?

Consult SREs for Davis AI tuning, DevOps engineers for Kubernetes data, and application teams for context. Test correlations in staging, integrate with Prometheus for metrics, and use analytics to optimize, ensuring accurate incident resolution in DevOps.

27. Which Dynatrace features support incident correlation?

• Davis AI for data correlation.
• OneAgent for metric collection.
• API for custom data queries.
• Dashboards for correlation visualization.
• Analytics for incident patterns.
• Prometheus for metric integration.
• PagerDuty for alert context.

28. How would you troubleshoot a Dynatrace monitoring outage?

In a monitoring outage scenario, verify OneAgent and ActiveGate connectivity in Kubernetes. Check network configurations, test in staging, and review logs for errors. Integrate with Prometheus for backup metrics and use analytics to restore observability in DevOps.

Collaborate with teams for resolution.

Explore serverless monitoring for outage prevention.

Compliance and Security Scenarios

29. What would you do if Dynatrace audit logs are incomplete?

If Dynatrace audit logs are incomplete in a compliance audit, verify OneAgent log collection and SIEM integrations. Test in staging, adjust log filters, and use analytics to identify gaps, ensuring comprehensive compliance logging in DevOps.

30. Why might Dynatrace fail to meet compliance requirements?

• Incomplete audit log configurations.
• Misconfigured SIEM integrations.
• Insufficient data retention policies.
• Limited Kubernetes log collection.
• Incorrect Davis AI compliance rules.
• Network issues delaying logs.
• Non-compliant data access controls.

31. When would you use Dynatrace for a compliance audit?

Use Dynatrace for a compliance audit when reviewing Kubernetes or CI/CD activities for regulatory adherence. Configure audit logs for traceability, integrate with SIEM for logging, and use analytics for reports, ensuring GDPR compliance in DevOps.

32. Where would you check for compliance data gaps in Dynatrace?

• OneAgent logs for collection errors.
• SIEM integrations for log forwarding.
• Kubernetes RBAC for access issues.
• Cluster storage for retention policies.
• API logs for audit queries.
• Dashboards for compliance visualization.
• Analytics for compliance trends.

33. Who would you involve in a Dynatrace compliance issue?

Involve compliance officers for audit log configurations, SREs for Dynatrace setup, and DevOps teams for CI/CD integration. Test in staging, align with SIEM for logging, and ensure regulatory adherence in DevOps workflows.

34. Which Dynatrace tools support compliance audits?

• Audit logs for regulatory tracking.
• SIEM integrations for log forwarding.
• Davis AI for compliance anomalies.
• API for custom audit queries.
• Dashboards for compliance visualization.
• Analytics for audit trends.
• Retention policies for regulations.

35. How would you ensure Dynatrace meets GDPR requirements?

In a GDPR compliance scenario, configure Dynatrace to encrypt data and enforce retention policies. Integrate with SIEM for secure logging, use Davis AI for anomaly detection, and test in staging to ensure compliance, protecting user data in DevOps.

Document configurations for audits.

Learn about compliance in DevOps.

36. What would you do if Dynatrace detects a security anomaly?

If Dynatrace detects a security anomaly in Kubernetes, use Davis AI to analyze root causes. Integrate with PagerDuty for alerts, review RBAC configurations, and test in staging to validate fixes, ensuring secure DevOps monitoring.

37. Why might Dynatrace miss a security vulnerability?

• Incomplete RBAC configurations.
• Limited OneAgent security monitoring.
• Misconfigured Davis AI rules.
• Network issues delaying alerts.
• Insufficient SIEM integrations.
• Inadequate Kubernetes log collection.
• Limited compliance log filters.

38. When would you configure Dynatrace for security monitoring?

Configure Dynatrace for security monitoring when detecting Kubernetes RBAC violations or CI/CD vulnerabilities. Set up Davis AI for anomaly detection, integrate with SIEM for logging, and use dashboards for visualization, ensuring secure DevOps workflows.

39. Where would you check for Dynatrace security monitoring issues?

• OneAgent logs for security data.
• RBAC settings in Kubernetes.
• SIEM integrations for log gaps.
• Davis AI rules for anomaly errors.
• PagerDuty for alert delivery issues.
• Dashboards for security visualization.
• Analytics for security trends.

40. Who would you consult for Dynatrace security issues?

Consult security engineers for RBAC configurations, SREs for Dynatrace setup, and DevOps teams for CI/CD integration. Test security rules in staging, align with SIEM for logging, and ensure secure monitoring in DevOps.

41. Which Dynatrace features support security monitoring?

• Davis AI for security anomaly detection.
• OneAgent for event collection.
• SIEM integrations for logging.
• API for custom security queries.
• Dashboards for security visualization.
• Analytics for security trends.
• PagerDuty for alert escalation.

42. How would you secure Dynatrace in a multi-cloud setup?

In a multi-cloud security scenario, configure Dynatrace to encrypt data and enforce RBAC in Kubernetes. Integrate with SIEM for secure logging, use Davis AI for anomaly detection, and test in staging to ensure secure monitoring across AWS, Azure, and GCP.

Validate configurations for compliance.

Explore container security for secure setups.

Synthetic Monitoring Scenarios

43. What would you do if Dynatrace synthetic tests fail in production?

If synthetic tests fail in production, review script configurations and Kubernetes endpoints. Check Davis AI for anomalies, test in staging, and integrate with CI/CD for automated fixes. Use analytics to identify patterns, ensuring reliable DevOps testing.

44. Why might Dynatrace synthetic tests produce false negatives?

• Incorrect test script configurations.
• Misaligned Kubernetes endpoints.
• Network latency affecting tests.
• Incomplete Davis AI anomaly rules.
• Limited CI/CD test integration.
• Inadequate test coverage in staging.
• Compliance restrictions on test data.

45. When would you use Dynatrace synthetic monitoring?

Use Dynatrace synthetic monitoring when validating application availability before Kubernetes deployments. Configure scripts for endpoint tests, integrate with CI/CD for automation, and use Davis AI for anomaly detection, ensuring reliable DevOps releases.

46. Where would you check for synthetic test failures?

• Test script logs for configuration errors.
• Kubernetes endpoints for connectivity.
• Davis AI rules for anomaly gaps.
• CI/CD pipeline for integration issues.
• Dashboards for test visualization.
• Analytics for test failure trends.
• Network logs for latency issues.

47. Who would you involve in synthetic test troubleshooting?

Involve QA engineers for test script validation, DevOps teams for CI/CD integration, and SREs for Dynatrace configurations. Test in staging, align with Kubernetes monitoring, and use analytics to resolve synthetic test issues in DevOps.

48. Which Dynatrace tools support synthetic monitoring?

• Synthetic scripts for browser tests.
• Davis AI for test anomaly detection.
• API for automated test workflows.
• Dashboards for test visualization.
• Analytics for test performance trends.
• CI/CD integrations for validation.
• Compliance logs for test audits.

49. How would you integrate Dynatrace synthetics with CI/CD?

In a CI/CD scenario, integrate Dynatrace synthetics by running pre-deploy tests on Kubernetes endpoints. Configure scripts in Jenkins, use Davis AI for anomaly detection, and automate validation, ensuring reliable releases in DevOps.

Test integrations in staging for accuracy.

Learn about deployment strategies for synthetic testing.

50. What would you do if Dynatrace synthetic tests are slow?

If synthetic tests are slow, optimize script execution and check network latency. Test in staging, reduce test complexity, and use analytics to identify bottlenecks, ensuring efficient synthetic monitoring in DevOps environments.

51. Why might Dynatrace user experience monitoring fail?

• Incomplete RUM configurations.
• Misaligned synthetic test scripts.
• Network issues affecting data collection.
• Incorrect Davis AI UX thresholds.
• Limited Kubernetes service monitoring.
• Compliance restrictions on UX data.
• Inadequate dashboard visualizations.

52. When would you use Dynatrace for user experience monitoring?

Use Dynatrace for user experience monitoring when tracking application performance for end-users. Configure real-user monitoring (RUM), integrate with synthetic tests, and use Davis AI for anomaly detection, ensuring reliable UX in DevOps.

53. Where would you check for UX monitoring issues?

• RUM configurations for data gaps.
• Synthetic test logs for errors.
• Kubernetes services for connectivity.
• Davis AI rules for UX anomalies.
• Dashboards for UX visualization.
• Analytics for UX performance trends.
• Network logs for latency issues.

54. Who would you consult for Dynatrace UX issues?

Consult frontend engineers for RUM configurations, QA teams for synthetic tests, and SREs for Dynatrace setup. Test in staging, align with Kubernetes monitoring, and use analytics to resolve UX issues in DevOps.

55. Which Dynatrace tools support UX monitoring?

• RUM for real-user metrics.
• Synthetic tests for simulated UX.
• Davis AI for UX anomaly detection.
• Dashboards for UX visualization.
• API for custom UX metrics.
• Analytics for UX trends.
• Compliance logs for privacy audits.

56. How would you troubleshoot Dynatrace UX monitoring failures?

In a UX monitoring failure scenario, verify RUM and synthetic test configurations. Check Kubernetes service connectivity, test in staging, and use Davis AI to analyze anomalies. Integrate with analytics for trends, ensuring reliable UX monitoring in DevOps.

Collaborate with frontend teams for resolution.

Explore observability practices for UX insights.

Log Analytics Scenarios

57. What would you do if Dynatrace log collection fails?

If Dynatrace log collection fails in Kubernetes, verify OneAgent deployment and log forwarding settings. Test in staging, integrate with SIEM for backups, and use analytics to identify gaps, ensuring comprehensive log monitoring in DevOps.

58. Why might Dynatrace log analytics miss critical errors?

• Incomplete OneAgent log collection.
• Misconfigured Kubernetes log filters.
• Network latency delaying logs.
• Incorrect Davis AI log rules.
• Limited SIEM integrations.
• Insufficient log retention policies.
• Inadequate dashboard visualizations.

59. When would you use Dynatrace for log analytics?

Use Dynatrace for log analytics when troubleshooting Kubernetes incidents or CI/CD failures. Configure OneAgent for log collection, Davis AI for anomaly detection, and dashboards for visualization, ensuring efficient DevOps log management.

60. Where would you check for log analytics issues?

• OneAgent logs for collection errors.
• Kubernetes pod logs for data gaps.
• SIEM integrations for forwarding issues.
• Davis AI rules for log anomalies.
• Dashboards for log visualization.
• Analytics for log performance trends.
• Network logs for latency issues.

61. Who would you involve in Dynatrace log issues?

Involve SREs for OneAgent configurations, DevOps teams for Kubernetes logs, and compliance officers for SIEM integrations. Test in staging, align with analytics, and ensure comprehensive log monitoring in DevOps workflows.

62. Which Dynatrace tools support log analytics?

• OneAgent for log collection.
• Davis AI for log anomaly detection.
• API for custom log queries.
• Dashboards for log visualization.
• Analytics for log patterns.
• SIEM integrations for compliance.
• Retention policies for audit logs.

63. How would you optimize Dynatrace log ingestion?

In a high-volume log scenario, optimize Dynatrace log ingestion by adjusting OneAgent sampling rates and log filters in Kubernetes. Test in staging, integrate with SIEM for offloading, and use analytics to reduce bottlenecks, ensuring efficient log monitoring in DevOps.

Scale cluster resources for performance.

Learn about log governance for optimization.

64. What would you do if Dynatrace logs are delayed?

If Dynatrace logs are delayed, check OneAgent configurations and network bandwidth. Test in staging, optimize log volumes, and integrate with SIEM for faster forwarding, ensuring real-time log analytics in DevOps environments.

65. Why might Dynatrace fail to correlate logs with metrics?

• Incomplete OneAgent data collection.
• Misconfigured Kubernetes log filters.
• Limited Prometheus metric integration.
• Incorrect Davis AI correlation rules.
• Network delays in data sync.
• Compliance restrictions on logs.
• Inadequate dashboard configurations.

66. When would you use Dynatrace for log correlation?

Use Dynatrace for log correlation when investigating Kubernetes incidents impacting applications. Configure Davis AI to link logs with metrics, integrate with Prometheus for data, and use dashboards for insights, ensuring effective DevOps troubleshooting.

67. Where would you check for log correlation issues?

• OneAgent logs for collection gaps.
• Kubernetes events for metric issues.
• Prometheus endpoints for data gaps.
• Davis AI rules for correlation errors.
• SIEM logs for forwarding issues.
• Dashboards for visualization gaps.
• Analytics for correlation trends.

68. Who would you consult for log correlation issues?

Consult SREs for Davis AI tuning, DevOps teams for Kubernetes logs, and data engineers for Prometheus integration. Test correlations in staging, align with analytics, and ensure accurate log correlation in DevOps workflows.

69. Which Dynatrace features support log correlation?

• Davis AI for log-metric correlation.
• OneAgent for log collection.
• API for custom log queries.
• Prometheus for metric integration.
• Dashboards for correlation visualization.
• Analytics for log patterns.
• SIEM for compliance logging.

70. How would you handle high-volume log ingestion in Dynatrace?

In a scenario with high-volume logs, configure OneAgent to filter critical Kubernetes logs. Optimize retention policies, integrate with SIEM for offloading, and use analytics to manage ingestion rates, ensuring efficient log processing in DevOps.

Test configurations in staging for scalability.

Explore vulnerability mitigation for log security.

API and Extension Scenarios

71. What would you do if Dynatrace API queries fail?

If Dynatrace API queries fail, verify endpoint configurations and authentication tokens. Check network connectivity, test in staging, and use analytics to identify bottlenecks, ensuring reliable API performance in DevOps workflows.

72. Why might Dynatrace API performance degrade?

• High query volumes overwhelming Cluster.
• Misconfigured API endpoints.
• Network latency affecting responses.
• Inadequate caching mechanisms.
• Limited Kubernetes metric integration.
• Compliance restrictions on API access.
• Incorrect rate-limiting settings.

73. When would you use the Dynatrace API?

Use the Dynatrace API when automating Kubernetes metric exports or CI/CD monitoring tasks. Configure endpoints for real-time data, test in staging, and integrate with Jenkins for pipeline insights, ensuring efficient DevOps automation.

74. Where would you check for Dynatrace API issues?

• API logs for query errors.
• Authentication settings for access issues.
• Network logs for connectivity problems.
• Kubernetes metrics for data gaps.
• Jenkins logs for integration issues.
• Dashboards for API performance.
• Analytics for query trends.

75. Who would you consult for Dynatrace API issues?

Consult SREs for API configurations, DevOps teams for CI/CD integration, and security engineers for authentication. Test in staging, align with analytics, and ensure reliable API performance in DevOps workflows.

76. Which Dynatrace API features support automation?

• Metric query endpoints for data export.
• Alert configuration for custom rules.
• Jenkins integration for CI/CD.
• Dashboards for API visualization.
• Analytics for API performance.
• Compliance tools for secure APIs.
• Extensions for custom integrations.

77. How would you automate Dynatrace monitoring with APIs?

In an automation scenario, use the Dynatrace API to query Kubernetes metrics and configure alerts for CI/CD pipelines. Integrate with Jenkins for real-time data, test in staging, and use dashboards for visualization, ensuring automated DevOps monitoring.

Validate API calls for reliability.

Learn about event-driven automation for APIs.

78. What would you do if Dynatrace extensions fail to load?

If Dynatrace extensions fail to load, verify extension configurations and compatibility with Kubernetes. Check network connectivity, test in staging, and use analytics to identify errors, ensuring reliable extension performance in DevOps.

79. Why might Dynatrace extensions cause monitoring gaps?

• Incompatible extension versions.
• Misconfigured Kubernetes integrations.
• Network issues affecting data sync.
• Incomplete Prometheus metric scraping.
• Limited API access for extensions.
• Compliance restrictions on data.
• Inadequate extension configurations.

80. When would you use Dynatrace extensions?

Use Dynatrace extensions when integrating with third-party tools like Prometheus for Kubernetes metrics. Configure extensions for custom data, test in staging, and integrate with dashboards for visualization, ensuring comprehensive DevOps monitoring.

81. Where would you check for Dynatrace extension issues?

• Extension logs for configuration errors.
• Kubernetes events for integration issues.
• Prometheus endpoints for metric gaps.
• API logs for extension queries.
• Dashboards for visualization issues.
• Analytics for extension performance.
• Network logs for connectivity problems.

82. Who would you consult for Dynatrace extension issues?

Consult SREs for extension configurations, DevOps teams for Kubernetes integration, and data engineers for Prometheus metrics. Test in staging, align with analytics, and ensure reliable extension functionality in DevOps workflows.

83. Which Dynatrace extensions support custom monitoring?

• Prometheus for metric integration.
• Custom plugins for Kubernetes data.
• API-driven extension endpoints.
• Dashboards for custom visualization.
• Analytics for extension trends.
• SIEM for compliance logging.
• PagerDuty for custom alerts.

84. How would you integrate Dynatrace extensions with Prometheus?

In a Prometheus integration scenario, configure Dynatrace extensions to scrape Kubernetes metrics via ActiveGate. Test in staging, use Davis AI for anomaly detection, and integrate with dashboards for visualization, ensuring comprehensive DevOps monitoring.

Validate configurations for accuracy.

Explore stateful monitoring for extensions.

Advanced Monitoring Scenarios

85. What would you do if Dynatrace misses serverless issues?

If Dynatrace misses serverless issues in AWS Lambda, verify OneAgent configurations and cloud API integrations. Test in staging, integrate with custom extensions, and use analytics to identify gaps, ensuring comprehensive serverless monitoring in DevOps.

86. Why might Dynatrace fail to monitor serverless functions?

• Incomplete OneAgent instrumentation.
• Misconfigured cloud API endpoints.
• Network latency affecting data.
• Incorrect Davis AI thresholds.
• Limited extension support for serverless.
• Compliance restrictions on data.
• Inadequate dashboard configurations.

87. When would you use Dynatrace for serverless monitoring?

Use Dynatrace for serverless monitoring when tracking AWS Lambda or Azure Functions performance. Configure OneAgent for instrumentation, Davis AI for anomaly detection, and dashboards for visualization, ensuring reliable serverless observability in DevOps.

88. Where would you check for serverless monitoring issues?

• OneAgent logs for instrumentation errors.
• Cloud API logs for connectivity issues.
• Davis AI rules for anomaly gaps.
• Extension logs for serverless data.
• Dashboards for visualization issues.
• Analytics for serverless trends.
• Network logs for latency problems.

89. Who would you consult for serverless monitoring issues?

Consult cloud engineers for serverless configurations, SREs for Dynatrace setup, and DevOps teams for integration. Test in staging, align with analytics, and ensure reliable serverless monitoring in DevOps workflows.

90. Which Dynatrace tools support serverless monitoring?

• OneAgent for serverless instrumentation.
• Davis AI for anomaly detection.
• API for custom serverless metrics.
• Dashboards for visualization.
• Analytics for performance trends.
• Extensions for cloud integrations.
• Compliance logs for audits.

91. How would you monitor serverless functions with Dynatrace?

In a serverless monitoring scenario, deploy OneAgent for AWS Lambda instrumentation. Configure Davis AI for anomaly detection, integrate with cloud APIs for metrics, and use dashboards for visualization, ensuring reliable serverless observability in DevOps.

Test configurations in staging for accuracy.

92. What would you do if Dynatrace API monitoring fails?

If Dynatrace API monitoring fails, verify endpoint configurations and Kubernetes service connectivity. Test in staging, integrate with custom extensions, and use analytics to identify gaps, ensuring reliable API monitoring in DevOps.

93. Why might Dynatrace miss API performance issues?

• Incomplete OneAgent instrumentation.
• Misconfigured API endpoints.
• Network latency affecting data.
• Incorrect Davis AI thresholds.
• Limited Kubernetes service monitoring.
• Compliance restrictions on API data.
• Inadequate dashboard visualizations.

94. When would you use Dynatrace for API monitoring?

Use Dynatrace for API monitoring when tracking performance in Kubernetes or cloud applications. Configure OneAgent for instrumentation, Davis AI for anomaly detection, and dashboards for visualization, ensuring reliable API observability in DevOps.

95. Where would you check for API monitoring issues?

• OneAgent logs for instrumentation errors.
• API endpoint logs for connectivity issues.
• Davis AI rules for anomaly gaps.
• Kubernetes services for data issues.
• Dashboards for visualization gaps.
• Analytics for API performance trends.
• Network logs for latency problems.

96. Who would you consult for API monitoring issues?

Consult backend engineers for API configurations, SREs for Dynatrace setup, and DevOps teams for Kubernetes integration. Test in staging, align with analytics, and ensure reliable API monitoring in DevOps workflows.

97. Which Dynatrace tools support API monitoring?

• OneAgent for API instrumentation.
• Davis AI for anomaly detection.
• API for custom metric queries.
• Dashboards for API visualization.
• Analytics for performance trends.
• Prometheus for metric integration.
• Compliance logs for audits.

98. How would you troubleshoot API performance issues in Dynatrace?

In an API performance issue scenario, use Dynatrace to analyze response times with OneAgent. Configure Davis AI for anomaly detection, integrate with Kubernetes for service data, and use dashboards for visualization, ensuring efficient API troubleshooting in DevOps.

Test configurations in staging for accuracy.

99. What would you do if Dynatrace dashboards fail to display data?

If Dynatrace dashboards fail to display data, verify OneAgent data collection and dashboard configurations. Check Kubernetes metrics, test in staging, and use analytics to identify gaps, ensuring reliable visualization in DevOps environments.

100. Why might Dynatrace dashboards show incomplete data?

• Incomplete OneAgent instrumentation.
• Misconfigured Kubernetes metrics.
• Network latency affecting data sync.
• Incorrect dashboard query settings.
• Limited Prometheus metric scraping.
• Compliance restrictions on data.
• Inadequate analytics configurations.