85+ Dynatrace Interview Questions and Answers [Cloud Monitoring – 2025]

Dynatrace Fundamentals

1. What is Dynatrace’s role in cloud monitoring?

Dynatrace is a comprehensive AI-powered platform for cloud monitoring, providing full-stack observability for applications, infrastructure, and user experience. It integrates with Kubernetes for container insights, Prometheus for metrics, and CI/CD pipelines for automated alerts. Dynatrace uses Davis AI for anomaly detection and root cause analysis, ensuring compliance and efficiency in multi-cloud DevOps environments, preparing candidates for senior SRE roles.

2. Why is Dynatrace essential for DevOps?

• Provides AI-driven anomaly detection.
• Integrates with Kubernetes for container monitoring.
• Supports CI/CD pipeline observability.
• Enables full-stack visibility in multi-cloud.
• Automates root cause analysis.
• Ensures compliance with audit logs.
• Scales for large-scale DevOps teams.

3. When should Dynatrace be deployed in Kubernetes?

Deploy Dynatrace in Kubernetes when scaling containerized applications require real-time observability. Use OneAgent for automatic instrumentation, integrate with Prometheus for metrics, and configure Davis AI for anomaly detection. This ensures proactive monitoring, compliance, and efficient incident resolution in DevOps environments.

4. Where does Dynatrace fit in a DevOps pipeline?

• Monitors CI/CD build and deploy stages.
• Tracks runtime in production clusters.
• Integrates with Kubernetes for pod metrics.
• Provides alerts in monitoring workflows.
• Enforces compliance in governance layers.
• Supports incident response in SRE practices.
• Delivers analytics for pipeline optimization.

5. Who benefits from Dynatrace expertise in SRE?

SREs, DevOps engineers, and cloud architects benefit from Dynatrace expertise for AI-driven observability and root cause analysis. It automates Kubernetes monitoring, integrates with CI/CD for alerts, and ensures compliance, enabling teams to maintain reliable infrastructure in multi-cloud DevOps.

6. Which Dynatrace components are critical for monitoring?

• OneAgent for automatic instrumentation.
• Davis AI for anomaly detection.
• ActiveGate for gateway functions.
• Cluster for data storage and analytics.
• API for custom integrations.
• Dashboards for visualization.
• Extensions for third-party tools.

7. How does Dynatrace use AI for root cause analysis?

Dynatrace uses Davis AI for root cause analysis by correlating metrics, logs, and traces in real-time. It detects anomalies in Kubernetes clusters, integrates with CI/CD for pipeline insights, and provides actionable recommendations, ensuring compliance monitoring in DevOps environments.

8. What is Dynatrace OneAgent’s deployment process?

Dynatrace OneAgent automates instrumentation for hosts, containers, and cloud services. Deploy via Helm charts for Kubernetes, integrate with CI/CD for automatic rollout, and configure for compliance. It collects metrics without code changes, supporting multi-cloud DevOps.

Test deployment in staging for reliability.

9. Why is Dynatrace’s Davis AI important?

• Detects anomalies with machine learning.
• Correlates events across stacks.
• Provides root cause recommendations.
• Integrates with Kubernetes for alerts.
• Reduces MTTR in incidents.
• Supports compliance with explanations.
• Scales for large DevOps environments.

10. When should Dynatrace be used for CI/CD monitoring?

Use Dynatrace for CI/CD monitoring when tracking pipeline performance and failures. Integrate with Jenkins for build insights, configure Davis AI for anomaly detection, and use dashboards for visualization, ensuring efficient DevOps workflows in multi-cloud setups.

11. Where does Dynatrace collect data?

Dynatrace collects data from hosts, containers, applications, and cloud services using OneAgent. It integrates with Kubernetes for pod metrics, Prometheus for custom data, and CI/CD for pipeline logs, ensuring comprehensive observability in DevOps.

12. Who configures Dynatrace for DevOps?

SRE engineers configure Dynatrace for DevOps, deploying OneAgent in Kubernetes and integrating with CI/CD. They set up Davis AI rules, test in staging, and collaborate with DevOps for alignment, ensuring reliable monitoring in multi-cloud environments.

13. Which Dynatrace tools support Kubernetes?

• OneAgent for container instrumentation.
• Davis AI for anomaly detection.
• Helm charts for deployment.
• API for custom integrations.
• Dashboards for cluster visualization.
• Extensions for Prometheus metrics.
• Analytics for performance trends.

14. How does Dynatrace integrate with Prometheus?

Dynatrace integrates with Prometheus by importing metrics via ActiveGate. Configure scraping endpoints for Kubernetes data, use Davis AI for correlation, and visualize in dashboards. This enhances observability for stateful applications in DevOps.

Test integrations in staging for accuracy.

15. What if Dynatrace OneAgent fails to deploy?

If Dynatrace OneAgent fails to deploy, check Helm chart configurations and Kubernetes permissions. Verify network access, test in staging, and review logs for errors. Update RBAC and integrate with CI/CD for automated rollout, ensuring reliable monitoring in DevOps.

Cloud Monitoring and Observability

16. What is Dynatrace’s full-stack observability?

Dynatrace’s full-stack observability covers applications, infrastructure, and user experience with AI-driven insights. It monitors Kubernetes pods, CI/CD pipelines, and cloud services, correlating data for root cause analysis, ensuring compliance and efficiency in DevOps environments.

17. Why use Dynatrace for multi-cloud monitoring?

• Unified visibility across AWS, Azure, GCP.
• AI correlation for cross-cloud insights.
• Automatic instrumentation for services.
• Supports Kubernetes in hybrid setups.
• Reduces MTTR with Davis AI.
• Ensures compliance with audit logs.
• Scales for enterprise DevOps.

18. When should Dynatrace monitor microservices?

Monitor microservices with Dynatrace when deploying distributed applications in Kubernetes. Use OneAgent for service instrumentation, Davis AI for anomaly detection, and dashboards for visualization, ensuring reliable observability in DevOps.

19. Where does Dynatrace collect cloud metrics?

Dynatrace collects cloud metrics from AWS, Azure, and GCP APIs via ActiveGate. It integrates with Kubernetes for pod data, Prometheus for custom metrics, and CI/CD for pipeline insights, ensuring comprehensive monitoring in DevOps.

20. Who configures Dynatrace for cloud monitoring?

Cloud architects configure Dynatrace for cloud monitoring, deploying ActiveGate for API integrations. They set up Davis AI rules, test in staging, and collaborate with DevOps for alignment, ensuring reliable multi-cloud observability.

21. Which Dynatrace features support observability?

• OneAgent for automatic instrumentation.
• Davis AI for root cause analysis.
• Dashboards for real-time visualization.
• API for custom integrations.
• Extensions for Prometheus data.
• Analytics for performance trends.
• Compliance tools for audit logs.

22. How does Dynatrace correlate monitoring data?

Dynatrace correlates monitoring data using Davis AI to link metrics, logs, and traces. It analyzes Kubernetes events, CI/CD logs, and cloud metrics, providing root cause insights for observability workflows in DevOps.

Test correlations in staging for accuracy.

23. What if Dynatrace misses cloud anomalies?

If Dynatrace misses cloud anomalies, verify OneAgent deployment and metric configurations. Check Davis AI rules, test in staging, and integrate with Prometheus for additional data. Use analytics to identify gaps, ensuring comprehensive monitoring in DevOps.

24. Why use Dynatrace for user experience monitoring?

• Tracks real-user monitoring (RUM) metrics.
• Integrates with synthetic monitoring.
• Correlates UX with infrastructure.
• Supports mobile app observability.
• Reduces MTTR for UX issues.
• Ensures compliance with privacy logs.
• Scales for global user bases.

25. When should Dynatrace monitor databases?

Monitor databases with Dynatrace when tracking query performance in Kubernetes environments. Use OneAgent for instrumentation, Davis AI for anomaly detection, and dashboards for visualization, ensuring reliable database observability in DevOps.

26. Where does Dynatrace deploy for cloud?

Dynatrace deploys for cloud via ActiveGate for API monitoring and OneAgent for services. It integrates with AWS, Azure, and GCP, supporting Kubernetes for container metrics, ensuring comprehensive cloud observability in DevOps.

27. Who sets up Dynatrace for database monitoring?

Database administrators set up Dynatrace for database monitoring, deploying OneAgent for instrumentation. They configure Davis AI rules, test in staging, and collaborate with DevOps for integration, ensuring reliable database performance in DevOps.

28. Which Dynatrace extensions support databases?

• OneAgent for SQL instrumentation.
• Davis AI for query anomaly detection.
• API for custom database metrics.
• Dashboards for query visualization.
• Analytics for performance trends.
• Integrations with Oracle, MySQL.
• Compliance tools for audit logs.

29. How does Dynatrace monitor log data?

Dynatrace monitors log data using OneAgent for automatic collection from Kubernetes pods. It correlates logs with metrics, uses Davis AI for anomaly detection, and supports log governance, ensuring compliant observability in DevOps.

30. What if Dynatrace log collection fails?

If Dynatrace log collection fails, verify OneAgent deployment and log forwarding settings. Check Kubernetes permissions, test in staging, and integrate with SIEM for backups. Use analytics to identify gaps, ensuring reliable log monitoring in DevOps.

AI-Driven Anomaly Detection

31. What is Davis AI’s role in Dynatrace?

Davis AI in Dynatrace automates anomaly detection and root cause analysis across applications and infrastructure. It correlates Kubernetes metrics, CI/CD logs, and cloud data, providing actionable insights for DevOps teams to resolve issues quickly.

32. Why use Davis AI for DevOps?

• Detects anomalies with machine learning.
• Correlates events across full-stack.
• Provides root cause recommendations.
• Integrates with Kubernetes for alerts.
• Reduces MTTR in incidents.
• Supports compliance with explanations.
• Scales for large DevOps environments.

33. When should Davis AI be enabled?

Enable Davis AI when monitoring complex Kubernetes clusters for proactive anomaly detection. Configure for CI/CD pipeline insights, test in staging, and integrate with dashboards for visualization, ensuring efficient DevOps incident resolution.

Review AI recommendations for accuracy.

34. Where does Davis AI analyze data?

Davis AI analyzes data in Dynatrace Cluster, correlating metrics from Kubernetes, cloud services, and applications. It uses machine learning for patterns, integrates with Prometheus, and provides dashboards for insights, ensuring comprehensive analysis in DevOps.

35. Who configures Davis AI rules?

SRE engineers configure Davis AI rules, defining thresholds for Kubernetes metrics. They test in staging, collaborate with DevOps for alignment, and use analytics to optimize, ensuring accurate anomaly detection in DevOps.

36. Which Davis AI features support DevOps?

• Anomaly detection for metrics.
• Root cause correlation across stacks.
• Automated problem notifications.
• Integration with Kubernetes events.
• Custom rule customization.
• Analytics for AI performance.
• API for automated AI workflows.

37. How does Davis AI integrate with CI/CD?

Davis AI integrates with CI/CD by analyzing pipeline metrics and logs. It detects anomalies in build times, correlates with Kubernetes deployments, and suggests resolutions, ensuring efficient DevOps, as in pipeline acceleration.

38. What if Davis AI misidentifies anomalies?

If Davis AI misidentifies anomalies, review training data and adjust thresholds. Test rules in staging, integrate with manual overrides, and use analytics to track errors, ensuring accurate detection in DevOps monitoring.

Collaborate with teams for validation.

39. Why use Davis AI for Kubernetes?

• Detects pod and node anomalies.
• Correlates with application metrics.
• Provides deployment impact analysis.
• Integrates with Prometheus data.
• Reduces MTTR for cluster issues.
• Supports compliance with explanations.
• Scales for large Kubernetes clusters.

40. When should Davis AI be tuned for production?

Tune Davis AI for production when monitoring Kubernetes clusters for accurate anomaly detection. Define custom rules, integrate with CI/CD for validation, and use analytics to optimize, ensuring reliable DevOps monitoring.

41. Where does Davis AI process data?

Davis AI processes data in Dynatrace Cluster, analyzing metrics from Kubernetes and cloud services. It correlates events, integrates with Prometheus, and provides dashboards for insights, ensuring comprehensive processing in DevOps.

42. Who tunes Davis AI for DevOps?

SRE engineers tune Davis AI for DevOps, adjusting thresholds for Kubernetes metrics. They test in staging, collaborate with DevOps for alignment, and use analytics to optimize, ensuring accurate anomaly detection in environments.

43. Which Davis AI tools support troubleshooting?

• Anomaly timelines for event correlation.
• Root cause graphs for visualization.
• Custom rules for troubleshooting.
• Integration with Kubernetes logs.
• Analytics for anomaly patterns.
• API for automated troubleshooting.
• Compliance explanations for audits.

44. How does Davis AI handle false positives?

Davis AI handles false positives by refining machine learning models with feedback. Adjust thresholds, test in staging, and integrate with manual overrides. Use analytics to track accuracy, ensuring reliable anomaly detection in DevOps monitoring.

Review false positives regularly for improvement.

45. What if Davis AI’s recommendations are ignored?

If Davis AI’s recommendations are ignored, document reasons in incident reports. Use analytics to track ignored alerts, integrate with CI/CD for validation, and refine models with feedback, ensuring effective anomaly resolution in DevOps.

Dynatrace in CI/CD and Kubernetes

46. What is Dynatrace’s role in CI/CD monitoring?

Dynatrace monitors CI/CD by tracking pipeline performance, build times, and failure rates. It integrates with Jenkins for real-time insights, correlates with Kubernetes deployments, and uses Davis AI for anomaly detection, ensuring efficient DevOps pipelines.

47. Why use Dynatrace for Kubernetes CI/CD?

• Tracks deployment performance metrics.
• Correlates pipeline failures with clusters.
• Provides AI-driven root cause analysis.
• Integrates with Jenkins for build data.
• Supports compliance with audit logs.
• Scales for large CI/CD workflows.
• Enhances DevOps pipeline reliability.

48. When should Dynatrace monitor CI/CD pipelines?

Monitor CI/CD pipelines with Dynatrace when scaling DevOps workflows for Kubernetes deployments. Use OneAgent for instrumentation, Davis AI for anomaly detection, and dashboards for visualization, ensuring reliable pipeline performance.

49. Where does Dynatrace integrate with Jenkins?

Dynatrace integrates with Jenkins via API for build metrics and OneAgent for runtime data. It correlates pipeline logs with Kubernetes deployments, supports dashboards for visibility, and uses Davis AI for analysis, ensuring efficient DevOps integration.

50. Who configures Dynatrace for CI/CD?

DevOps engineers configure Dynatrace for CI/CD, deploying OneAgent in Jenkins and integrating with Kubernetes. They set up Davis AI rules, test in staging, and collaborate with SREs for alignment, ensuring reliable pipeline monitoring.

51. Which Dynatrace features support CI/CD?

• OneAgent for pipeline instrumentation.
• Davis AI for build anomaly detection.
• API for Jenkins integrations.
• Dashboards for pipeline visualization.
• Analytics for performance trends.
• Extensions for custom CI/CD data.
• Compliance tools for audit logs.

52. How does Dynatrace monitor Kubernetes deployments?

Dynatrace monitors Kubernetes deployments by instrumenting pods with OneAgent. It tracks resource usage, correlates with CI/CD pipelines, and uses Davis AI for anomaly detection, ensuring reliable deployments in DevOps, as in deployment automation.

Test monitoring in staging for accuracy.

53. What if Dynatrace misses deployment anomalies?

If Dynatrace misses deployment anomalies, verify OneAgent rollout and Kubernetes integrations. Check Davis AI rules, test in staging, and integrate with Prometheus for additional data. Use analytics to identify gaps, ensuring comprehensive deployment monitoring in DevOps.

54. Why use Dynatrace for pipeline performance?

• Tracks build and deploy times.
• Correlates with Kubernetes metrics.
• Provides AI-driven bottleneck analysis.
• Integrates with Jenkins for data.
• Supports compliance with logs.
• Scales for large CI/CD workflows.
• Enhances DevOps pipeline efficiency.

55. When should Dynatrace be used for pipeline alerts?

Use Dynatrace for pipeline alerts when Jenkins builds exceed thresholds. Configure Davis AI for anomaly detection, integrate with PagerDuty for notifications, and use dashboards for visualization, ensuring proactive DevOps pipeline management.

56. Where does Dynatrace collect CI/CD data?

Dynatrace collects CI/CD data from Jenkins APIs and OneAgent in pipelines. It correlates with Kubernetes deployments, supports dashboards for visualization, and uses Davis AI for analysis, ensuring comprehensive pipeline observability in DevOps.

57. Who sets up Dynatrace for pipeline monitoring?

DevOps engineers set up Dynatrace for pipeline monitoring, deploying OneAgent in Jenkins. They configure Davis AI rules, test in staging, and collaborate with SREs for integration, ensuring reliable CI/CD observability in DevOps.

58. Which Dynatrace extensions support CI/CD?

• Jenkins extension for build data.
• API for custom pipeline metrics.
• Davis AI for anomaly detection.
• Dashboards for pipeline visualization.
• Analytics for performance trends.
• Integrations with GitLab, Azure DevOps.
• Compliance tools for audit logs.

59. How does Dynatrace analyze pipeline bottlenecks?

Dynatrace analyzes pipeline bottlenecks by correlating Jenkins build times with Kubernetes deployments. Use Davis AI for anomaly detection, dashboards for visualization, and pipeline acceleration insights, ensuring efficient DevOps workflows.

60. What if Dynatrace’s pipeline data is delayed?

Dynatrace’s pipeline data is delayed. Verify Jenkins API integrations, check network latency, and test in staging. Update OneAgent configurations and use analytics to identify bottlenecks, ensuring real-time pipeline monitoring in DevOps.

Advanced Dynatrace Features

61. What is Dynatrace’s synthetic monitoring?

Dynatrace’s synthetic monitoring simulates user interactions to test application availability. It integrates with Kubernetes for endpoint checks, supports CI/CD for pre-deployment validation, and uses Davis AI for anomaly detection, ensuring proactive DevOps monitoring.

62. Why use Dynatrace synthetics for DevOps?

• Simulates real-user scenarios for testing.
• Integrates with CI/CD for pre-deploy checks.
• Detects availability issues early.
• Supports Kubernetes endpoint monitoring.
• Reduces MTTR with AI analysis.
• Ensures compliance with test logs.
• Scales for global synthetic tests.

63. When should synthetic monitoring be enabled?

Enable synthetic monitoring when validating Kubernetes deployments pre-production. Configure scripts for endpoint tests, integrate with CI/CD for automation, and use Davis AI for anomaly detection, ensuring reliable DevOps releases.

64. Where does Dynatrace run synthetics?

Dynatrace runs synthetics from global vantage points, simulating user access to applications. It integrates with Kubernetes for service checks, supports CI/CD for validation, and provides dashboards for results, ensuring comprehensive DevOps testing.

65. Who configures Dynatrace synthetics?

QA engineers configure Dynatrace synthetics, creating scripts for application tests. They integrate with CI/CD, test in staging, and collaborate with DevOps for alignment, ensuring reliable synthetic monitoring in DevOps.

66. Which Dynatrace synthetics features support DevOps?

• Scripted browser tests for UX.
• API endpoint validations.
• Integration with CI/CD pipelines.
• Davis AI for synthetic anomalies.
• Dashboards for test results.
• Analytics for synthetic trends.
• API for automated test workflows.

67. How does Dynatrace synthetics integrate with CI/CD?

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

Test integrations in staging for accuracy.

68. What if 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.

69. Why use Dynatrace for log analytics?

• Correlates logs with metrics and traces.
• Uses AI for log anomaly detection.
• Supports Kubernetes log collection.
• Integrates with SIEM for compliance.
• Reduces manual log review time.
• Scales for large log volumes.
• Enhances DevOps troubleshooting.

70. When should Dynatrace analyze logs?

Analyze logs with Dynatrace when troubleshooting Kubernetes incidents. Use OneAgent for collection, Davis AI for correlation, and dashboards for visualization, ensuring efficient DevOps log management.

71. Where does Dynatrace store log data?

Dynatrace stores log data in its Cluster, accessible via API. It integrates with SIEM for forwarding, supports retention policies for compliance, and uses dashboards for analysis, ensuring secure log storage in DevOps.

72. Who configures Dynatrace log rules?

SRE engineers configure Dynatrace log rules, defining filters for Kubernetes logs. They test in staging, collaborate with DevOps for alignment, and use analytics to optimize, ensuring effective log monitoring in DevOps.

73. Which Dynatrace log features support DevOps?

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

74. How does Dynatrace correlate logs with metrics?

Dynatrace correlates logs with metrics using Davis AI to link Kubernetes events and Prometheus data. It provides root cause insights, integrates with dashboards for visualization, and supports log correlation, ensuring comprehensive DevOps monitoring.

75. What if Dynatrace log ingestion is slow?

Dynatrace log ingestion is slow. Verify OneAgent deployment, check network bandwidth, and optimize log volumes. Test in staging, integrate with SIEM for offloading, and use analytics to identify bottlenecks, ensuring efficient log monitoring in DevOps.

Scale cluster resources for high volumes.

Dynatrace Extensions and API

76. What is Dynatrace’s API role?

Dynatrace’s API enables custom integrations for DevOps, automating metric queries and alert configurations. It supports Kubernetes data export, CI/CD pipeline insights, and compliance reporting, ensuring flexible monitoring in multi-cloud environments.

77. Why use Dynatrace extensions for DevOps?

• Extend monitoring to third-party tools.
• Integrate with Prometheus for metrics.
• Support custom Kubernetes integrations.
• Automate compliance reporting.
• Enhance CI/CD with extensions.
• Scale for multi-cloud needs.
• Facilitate troubleshooting workflows.

78. When should Dynatrace API be used?

Use Dynatrace API when automating DevOps tasks like querying Kubernetes metrics or integrating with CI/CD. Configure for real-time data export, test in staging, and ensure compliance, streamlining monitoring workflows.

79. Where does Dynatrace API integrate?

Dynatrace API integrates with CI/CD tools like Jenkins, Kubernetes for metrics export, and SIEM for logs. It supports dashboards for visualization, ensuring flexible monitoring in DevOps environments.

80. Who configures Dynatrace API?

SRE engineers configure Dynatrace API, setting up endpoints for Kubernetes data and CI/CD integrations. They test in staging, collaborate with DevOps for alignment, and use analytics to optimize, ensuring reliable API usage in DevOps.

81. Which Dynatrace API features support DevOps?

• Metric query endpoints for data export.
• Alert configuration for custom rules.
• Integration with Jenkins for CI/CD.
• Dashboards for API-driven visualization.
• Analytics for API performance.
• Compliance tools for secure API.
• Extensions for third-party integrations.

82. How does Dynatrace API integrate with CI/CD?

Dynatrace API integrates with CI/CD by exporting metrics to Jenkins for build analysis. Configure endpoints for pipeline data, use Davis AI for insights, and automate reporting, ensuring efficient DevOps, as in pipeline standardization.

Test API calls in staging for reliability.

83. What if Dynatrace API queries are slow?

Dynatrace API queries are slow. Optimize endpoint configurations, check data volumes, and test in staging. Integrate with caching layers, use analytics to identify bottlenecks, and scale cluster resources for efficient API performance in DevOps.

Review query complexity for improvements.

84. Why use Dynatrace for compliance monitoring?

• Generates audit logs for actions.
• Integrates with SIEM for compliance.
• Supports retention policies for data.
• Provides analytics for compliance trends.
• Ensures traceability in DevOps workflows.
• Facilitates regulatory audits.
• Scales for enterprise compliance needs.

85. When should Dynatrace be used for audits?

Use Dynatrace for audits during compliance reviews or post-incident analysis. Configure audit logs for traceability, integrate with SIEM for logging, and use analytics for reports, ensuring adherence to standards like GDPR in DevOps.

86. Where does Dynatrace store audit data?

Dynatrace stores audit data in its Cluster, accessible via API. It integrates with SIEM for forwarding, supports retention policies for compliance, and provides dashboards for analysis, ensuring secure audit storage in DevOps.

87. Who manages Dynatrace’s compliance reporting?

Compliance officers manage Dynatrace’s compliance reporting, configuring logs and analytics for regulatory standards. They test in staging, collaborate with DevOps for alignment, and integrate with SIEM, ensuring accurate reporting in DevOps.