SRE Certification Interview Questions [2025]

Site Reliability Engineering (SRE) certifications, such as Google SRE, AWS DevOps, and Azure DevOps, validate skills in building resilient, scalable systems. This guide provides 103 scenario-based questions tailored for SRE interviews, spanning system design, incident response, automation, observability, cloud platforms, chaos engineering, capacity planning, and security. Designed for DevOps professionals, it offers practical solutions to master cloud-native environments, ensuring success in high-stakes technical interviews.

System Design and Scalability

1. What ensures high availability in system design?

High availability relies on redundancy, load balancing, and proactive monitoring. Define SLIs like 99.99% uptime, set SLOs, and use Prometheus for metrics collection. Configure Kubernetes with kubectl for auto-scaling and NGINX for traffic distribution. Automate provisioning with Terraform to maintain consistency across environments. This minimizes downtime, supports scalability, and aligns with SLO alignment for cloud-native systems, meeting certification standards.

2. How do you design a fault-tolerant microservices architecture?

• Orchestrate microservices with Kubernetes for auto-recovery.
• Implement circuit breakers using Istio to manage failures.
• Cache data in Redis to reduce database load.
• Monitor with Prometheus and Grafana for real-time insights.
• Validate configurations with terraform plan for consistency.

This ensures fault tolerance, scalability, and rapid recovery, enabling reliable CI/CD pipelines in cloud-native environments, critical for SRE certification scenarios.

3. Why prefer Kubernetes for container orchestration?

Kubernetes provides automated scaling, self-healing, and service discovery. Configure clusters with kubectl, monitor with Prometheus, and use StatefulSets for stateful applications. It reduces operational complexity, ensures high availability, and integrates seamlessly with CI/CD pipelines via GitLab. This makes it ideal for cloud-native deployments in AWS, Azure, or GCP, aligning with SRE certification focus on scalable, resilient systems.

4. When do you implement load balancing?

Implement load balancing to distribute traffic and prevent server overload. Use NGINX or AWS ELB, monitor with Datadog, and validate with curl. Load balancing enhances scalability, ensures high availability, and supports microservices in cloud-native systems. This is critical for SRE certifications, which emphasize expertise in managing high-traffic scenarios with minimal downtime.

5. Where do you store application configurations?

• Store configurations in GitLab for version control.
• Use Kubernetes ConfigMaps for runtime settings.
• Secure sensitive data with HashiCorp Vault.
• Monitor integrity with Prometheus.

This ensures consistent, secure, and traceable configurations, supporting scalable CI/CD pipelines in cloud-native environments, a key requirement for SRE certifications.

6. Who designs scalable SRE systems?

SREs collaborate with architects to design scalable systems. They use Terraform for infrastructure, Kubernetes for orchestration, and Prometheus for monitoring. Validate configurations with terraform plan and deploy with kubectl. This approach ensures scalability, fault tolerance, and reliability in CI/CD pipelines, aligning with certification objectives for managing cloud-native or high-availability systems effectively.

7. Which tools enhance system scalability?

• Kubernetes scales containers dynamically with kubectl.
• Terraform provisions infrastructure across clouds.
• Prometheus monitors performance and triggers alerts.
• NGINX balances traffic for high availability.

Integrate via terraform apply and kubectl, ensuring scalable CI/CD pipelines. Monitor with Grafana to support cloud-native deployments, vital for SRE certifications.

8. How do you optimize API performance?

• Cache responses with Redis to reduce latency.
• Use rate limiting with NGINX to manage traffic.
• Optimize backend queries with psql EXPLAIN plans.
• Monitor with Prometheus for performance insights.

Validate with curl and deploy with kubectl, ensuring low-latency APIs in cloud-native systems, critical for microservice architectures in SRE certifications.

9. What factors influence system scalability?

Scalability depends on resource allocation, load balancing, and monitoring. Use Kubernetes HPA for dynamic scaling, NGINX for traffic distribution, and Prometheus for metrics. Validate with terraform plan and deploy with kubectl. This ensures systems handle increased loads efficiently, a core competency for SRE certifications focused on cloud-native and high-traffic environments.

10. Why use distributed systems for scalability?

Distributed systems enhance scalability by spreading workloads across nodes. Configure Kubernetes with kubectl for orchestration, monitor with Prometheus, and balance with NGINX. Validate with terraform plan to ensure consistency. This approach improves fault tolerance and supports CI/CD pipelines, aligning with SRE certification goals for reliable, scalable cloud-native systems.

11. When do you scale infrastructure?

Scale infrastructure when metrics indicate high CPU, memory usage, or SLO breaches. Use Prometheus for monitoring, Kubernetes HPA with kubectl for scaling, and Grafana for visualization. Validate with terraform plan to ensure consistency. This prevents performance degradation, a critical skill for SRE certifications in managing dynamic cloud environments.

12. Where do you deploy scalable applications?

• Deploy to Kubernetes clusters for container orchestration.
• Use AWS EKS or Azure AKS for managed services.
• Monitor with Prometheus for performance insights.
• Validate deployments with kubectl get pods.

This ensures scalability and reliability, supporting CI/CD pipelines in cloud-native environments, essential for SRE certifications.

13. Who validates system scalability?

SREs validate scalability with architects and developers. Use Prometheus to monitor metrics, Grafana for visualization, and Kubernetes for scaling with kubectl. Validate with terraform plan and document in Confluence. This ensures systems meet SLOs, a key focus for SRE certifications in cloud-native and high-availability environments.

14. Which metrics guide scalability decisions?

• CPU and memory usage indicate resource constraints.
• Latency measures user experience impact.
• Traffic volume predicts scaling needs.
• Error rates signal system stress.

Collect with Prometheus and visualize with Grafana, ensuring scalable CI/CD pipelines for SRE certifications.

15. How do you ensure system redundancy?

• Deploy multiple replicas with kubectl in Kubernetes.
• Use multi-AZ setups in AWS or Azure.
• Monitor failover with Prometheus and Grafana.
• Validate with terraform plan for consistency.

This minimizes downtime, ensuring high availability in cloud-native systems, critical for SRE certifications.

Incident Response

16. What mitigates a service outage?

Analyze logs with journalctl -u service to identify issues, rollback with kubectl rollout undo, and notify via Slack. Use Prometheus for root cause analysis and apply fixes with kubectl. Document in Confluence to prevent recurrence. This minimizes downtime and ensures quick recovery in cloud-native environments, aligning with SRE certification skills for effective incident response.

17. How do you manage on-call rotations?

• Schedule with PagerDuty for equitable distribution.
• Monitor alerts with Prometheus for rapid response.
• Document procedures in Confluence for consistency.
• Communicate updates via Slack for alignment.
• Validate fixes with kubectl apply for reliability.

This ensures efficient incident handling, supporting high-availability systems in CI/CD pipelines for SRE certifications.

18. Why conduct blameless postmortems?

Blameless postmortems promote learning without blame, identifying root causes. Analyze logs with journalctl, review metrics in Prometheus, and document in Confluence. Collaborate via Slack, implement fixes with kubectl, and monitor with Grafana. This strengthens system resilience, a key focus for SRE certifications in reliable cloud-native or Kubernetes environments.

19. When do you escalate critical incidents?

Escalate incidents when they exceed team expertise or breach SLOs. Use PagerDuty for escalation, monitor with Prometheus, and communicate via Slack. Document in Confluence and validate fixes with kubectl apply. This ensures rapid resolution, minimizing downtime in CI/CD pipelines, a critical skill for SRE certifications like AWS DevOps or Google SRE.

20. Where do you centralize incident logs?

• Store logs in ELK stack for analysis via Kibana.
• Access system logs with journalctl -u service.
• Use CloudWatch for AWS-based log aggregation.
• Monitor log integrity with Prometheus.

Centralized logs ensure traceability, supporting efficient incident management in cloud-native environments for SRE certifications.

21. Who coordinates incident response?

SREs coordinate incident response, engaging developers and ops teams. Use PagerDuty for scheduling, Prometheus for monitoring, and Slack for communication. Implement fixes with kubectl or Terraform and document in Confluence. This ensures organized resolution, maintaining reliability in CI/CD pipelines, aligning with SRE certification goals.

22. Which metrics prioritize incident resolution?

• Track latency and error rates with Prometheus.
• Monitor SLO compliance for critical services.
• Analyze resource usage with Grafana dashboards.
• Correlate logs in ELK for root cause analysis.

Metrics guide rapid fixes with kubectl or Terraform, ensuring minimal downtime in CI/CD workflows for SRE certifications.

23. How do you minimize MTTR in incidents?

Automated Prometheus alerts detect issues early, while ELK centralizes logs for analysis. Confluence runbooks guide fixes, implemented with kubectl rollout.

Monitor with Grafana, validate with unit tests, and communicate via Slack. This approach reduces MTTR, ensuring quick resolution in CI/CD pipelines for cloud-native or Kubernetes environments, a critical skill for SRE certifications.

24. What defines an effective incident response?

Effective incident response involves rapid detection, clear communication, and thorough documentation. Use Prometheus for alerts, Slack for updates, and Confluence for runbooks. Implement fixes with kubectl and validate with unit tests. Monitor with Grafana to ensure resolution. This minimizes downtime and prevents recurrence, aligning with SRE certification requirements for reliable cloud-native systems.

25. Why prioritize user impact in incidents?

Prioritizing user impact ensures SLO compliance and maintains trust. Monitor with Prometheus to assess latency and errors, communicate via Slack, and document in Confluence. Implement fixes with kubectl and validate with unit tests. This approach minimizes disruption, a core competency for SRE certifications in high-traffic, cloud-native environments.

26. When do you involve stakeholders?

Involve stakeholders during critical incidents or SLO breaches. Use Slack for real-time updates, PagerDuty for escalations, and Confluence for documentation. Monitor with Prometheus and implement fixes with kubectl. This ensures transparency and alignment, maintaining reliability in CI/CD pipelines, a key focus for SRE certifications in cloud-native systems.

27. Where do you store postmortem findings?

• Store findings in Confluence for team access.
• Log metrics in Prometheus for analysis.
• Visualize trends in Grafana dashboards.
• Archive logs in ELK for traceability.

This ensures actionable insights, supporting continuous improvement in CI/CD pipelines for SRE certifications.

28. Who reviews incident response processes?

SREs and managers review processes, analyzing Prometheus metrics and Confluence postmortems. Collaborate via Slack, implement improvements with kubectl, and validate with unit tests. This ensures continuous refinement, maintaining reliability in CI/CD pipelines, a critical skill for SRE certifications in cloud-native environments.

29. Which tools enhance incident response?

• Prometheus triggers alerts for rapid detection.
• PagerDuty manages on-call escalations.
• ELK centralizes logs for analysis.
• Grafana visualizes incident metrics.

Integrate with kubectl and Slack, ensuring efficient resolution in cloud-native CI/CD pipelines, vital for SRE certifications.

30. How do you test incident response plans?

Test plans with chaos engineering tools like Chaos Monkey, simulating failures via kubectl. Monitor with Prometheus, analyze with Grafana, and document in Confluence. Validate with unit tests and communicate via Slack. This ensures plans are robust, aligning with SRE certification requirements for reliable cloud-native systems.

Automation and CI/CD

31. What automates infrastructure deployment?

Terraform and Ansible automate infrastructure with terraform apply and ansible-playbook. Validate with terraform plan, monitor with Prometheus, and deploy with kubectl. Automation ensures consistency, reduces errors, and supports scalable CI/CD pipelines, aligning with SRE certification requirements for efficient cloud-native or Kubernetes-based system deployments.

32. How do you build CI/CD pipelines?

• Define pipelines in .gitlab-ci.yml for automation.
• Use Docker for isolated job environments.
• Deploy to Kubernetes with kubectl apply.
• Monitor health with Prometheus and Grafana.
• Validate with gitlab-ci lint for correctness.

This ensures automated builds and deployments, supporting reliable CI/CD workflows for SRE certifications.

33. Why use GitOps for deployments?

GitOps ensures declarative deployments with Git as the single source of truth. Use ArgoCD for automation, validate with git diff, and monitor with Prometheus. This reduces errors, enhances traceability, and supports scalable CI/CD pipelines, a key focus for SRE certifications like Google SRE or Azure DevOps.

34. When do you use canary deployments?

Use canary deployments to test updates on a small user subset. Configure with kubectl in Kubernetes, monitor with Prometheus, and switch traffic with NGINX. Rollback with kubectl rollout undo if needed. This minimizes risks, ensuring reliable CI/CD pipelines for cloud-native systems, critical for SRE certification scenarios.

35. Where do you store pipeline secrets?

• Store in GitLab CI/CD variables with encryption.
• Use HashiCorp Vault for secure secret management.
• Restrict with IAM roles in AWS or Azure.
• Validate with vault read commands.

Secure storage prevents leaks, ensuring compliant CI/CD pipelines for SRE certifications.

36. Who secures CI/CD pipelines?

SREs and DevOps engineers secure pipelines with GitLab protected variables, vault for secrets, and SAST scans. Monitor with Prometheus and validate with gitlab-ci lint. This ensures compliance and security in CI/CD pipelines, a critical skill for SRE certifications like AWS DevOps.

37. Which tools automate CI/CD pipelines?

• GitLab automates with .gitlab-ci.yml configurations.
• Jenkins orchestrates workflows with plugins.
• Terraform provisions consistent infrastructure.
• Docker ensures reproducible environments.

Integrate with kubectl and monitor with Prometheus, ensuring scalable CI/CD pipelines for SRE certifications.

38. How do you optimize CI/CD performance?

Cache dependencies in .gitlab-ci.yml to speed up builds. Use parallel jobs and scale runners with Kubernetes.

Monitor with Prometheus, validate with gitlab-ci lint, and analyze with Grafana. Lightweight Docker images reduce overhead, ensuring fast CI/CD pipelines. This approach minimizes delays, aligning with SRE certification requirements for efficient deployment workflows.

39. What ensures pipeline reliability?

Pipeline reliability requires automated testing, monitoring, and rollback capabilities. Use .gitlab-ci.yml for automation, Prometheus for monitoring, and kubectl for deployments. Validate with gitlab-ci lint and document in Confluence. This ensures consistent CI/CD pipelines, reducing errors and downtime, a core competency for SRE certifications in cloud-native systems.

40. Why automate repetitive SRE tasks?

Automation reduces errors and saves time. Use Terraform for infrastructure, Ansible for configuration, and .gitlab-ci.yml for CI/CD. Monitor with Prometheus and validate with terraform plan. This ensures consistency and scalability, freeing SREs for strategic tasks, a key focus for certifications in cloud-native environments.

41. When do you trigger automated rollbacks?

Trigger rollbacks when deployments fail SLOs or introduce errors. Configure kubectl rollout undo in Kubernetes, monitor with Prometheus, and validate with unit tests. Notify via Slack and document in Confluence. This minimizes downtime, ensuring reliable CI/CD pipelines, critical for SRE certifications in cloud-native systems.

42. Where do you define CI/CD workflows?

• Define workflows in .gitlab-ci.yml for GitLab.
• Use azure-pipelines.yml for Azure DevOps.
• Store in Git repositories for version control.
• Validate with gitlab-ci lint or az pipelines validate.

This ensures traceable, automated CI/CD pipelines, aligning with SRE certification requirements.

43. Who maintains CI/CD pipelines?

SREs and DevOps engineers maintain pipelines, configuring .gitlab-ci.yml for automation. Monitor with Prometheus, validate with gitlab-ci lint, and document in Confluence. Collaborate via Slack to address issues. This ensures reliable, scalable CI/CD pipelines, a critical skill for SRE certifications.

44. Which deployment strategies reduce risk?

• Canary deployments test updates on small subsets.
• Blue-green deployments ensure zero downtime.
• Rollback with kubectl rollout undo for safety.
• Monitor with Prometheus for stability.

These strategies minimize disruptions, essential for SRE certifications in cloud-native environments.

45. How do you integrate testing in CI/CD?

Integrate testing in .gitlab-ci.yml with unit tests via pytest. Run SAST scans for security, monitor with Prometheus, and validate with gitlab-ci lint. Deploy with kubectl and document in Confluence. This ensures code quality and reliability, aligning with SRE certification requirements for robust CI/CD pipelines in cloud-native systems.

Monitoring and Observability

46. What monitors system health?

Prometheus and Grafana monitor CPU, memory, and latency. Configure prometheus.yml for metrics, visualize with Grafana, and set alerts with promtool. Centralize logs with ELK and access via journalctl -u service. This ensures proactive issue detection, critical for SRE certifications focused on reliable cloud-native or Kubernetes systems.

47. How do you configure outage alerts?

• Configure alerts in prometheus.yml for thresholds.
• Integrate PagerDuty for real-time notifications.
• Test with promtool test rules for accuracy.
• Visualize triggers in Grafana for quick response.
• Centralize logs in ELK for analysis.

This ensures rapid outage detection, a key skill for SRE certifications.

48. Why is observability critical?

Observability enables proactive issue detection with metrics, logs, and traces. Use Prometheus for metrics, ELK for logs, and Jaeger for tracing. Visualize with Grafana and set alerts with promtool. This ensures reliability in CI/CD pipelines, a core competency for SRE certifications in cloud-native or Kubernetes environments.

49. When do you analyze system logs?

Analyze logs during incidents or performance issues with journalctl -u service or Kibana. Centralize with ELK, monitor with Prometheus, and correlate with Grafana. This ensures quick root cause identification, maintaining reliability in CI/CD pipelines, a critical skill for SRE certification exams.

50. Where do you visualize system metrics?

• Grafana dashboards display latency and resource usage.
• Prometheus collects real-time metrics.
• ELK visualizes logs alongside metrics.
• CloudWatch provides cloud-specific insights.

Access via Grafana or Kibana, ensuring comprehensive observability in CI/CD pipelines for SRE certifications.

51. Who monitors production environments?

SREs monitor production with Prometheus, Grafana, and ELK. Set alerts with promtool, access logs with journalctl, and visualize in Grafana. Collaborate via Slack for updates. This ensures uptime and reliability, a key focus for SRE certifications like AWS DevOps or Azure DevOps.

52. Which metrics ensure system reliability?

• Uptime ensures SLO compliance.
• Error rates identify application issues.
• Latency measures user experience.
• Resource usage detects bottlenecks.

Collect with Prometheus and Grafana, ensuring reliable CI/CD pipelines for SRE certifications.

53. How do you reduce monitoring overhead?

Filter prometheus.yml metrics to focus on critical data. Use lightweight Telegraf agents and aggregate logs with ELK.

Visualize with Grafana, validate with promtool, and monitor with kubectl top pods. This minimizes costs while ensuring observability, a key skill for SRE certifications in cost-effective cloud-native systems.

54. What improves observability in microservices?

Distributed tracing with Jaeger, metrics with Prometheus, and logs with ELK improve observability. Visualize with Grafana, set alerts with promtool, and deploy with kubectl. This ensures comprehensive insights, reducing debugging time in CI/CD pipelines, a critical focus for SRE certifications in cloud-native environments.

55. Why use distributed tracing?

Distributed tracing identifies latency in microservices. Use Jaeger for traces, Prometheus for metrics, and Grafana for visualization. Correlate with ELK logs and validate with unit tests. This ensures quick issue resolution, maintaining reliability in CI/CD pipelines, a key competency for SRE certifications in cloud-native systems.

56. When do you update monitoring configurations?

Update configurations when SLOs change or new services are added. Modify prometheus.yml for metrics, adjust Grafana dashboards, and validate with promtool. Monitor with kubectl and document in Confluence. This ensures observability aligns with system needs, critical for SRE certifications in dynamic cloud environments.

57. Where do you store monitoring data?

• Store metrics in Prometheus for real-time analysis.
• Archive logs in ELK for long-term searchability.
• Use CloudWatch for cloud-specific data.
• Visualize trends in Grafana dashboards.

This ensures accessible, reliable data for CI/CD pipelines, vital for SRE certifications.

58. Who configures monitoring tools?

SREs configure Prometheus, Grafana, and ELK. Set up prometheus.yml for metrics, create Grafana dashboards, and integrate with kubectl for Kubernetes. Validate with promtool and document in Confluence. This ensures comprehensive observability, a critical skill for SRE certifications in cloud-native environments.

59. Which tools support observability?

• Prometheus collects real-time metrics.
• Grafana visualizes performance trends.
• ELK aggregates logs for analysis.
• Jaeger traces microservice interactions.

Integrate with kubectl and Slack, ensuring robust observability for SRE certifications.

60. How do you validate monitoring alerts?

Test alerts with promtool test rules to ensure accuracy. Configure prometheus.yml for thresholds, integrate with PagerDuty, and visualize in Grafana. Monitor with kubectl and document in Confluence. This ensures reliable alerts, reducing false positives in CI/CD pipelines, a key focus for SRE certifications in cloud-native systems.

Cloud Platforms

61. What provisions cloud infrastructure?

Terraform provisions infrastructure with .tf files. Execute with terraform apply, validate with terraform plan, and monitor with CloudWatch. Integrate with Kubernetes via kubectl for orchestration. This ensures scalability and consistency in CI/CD pipelines, a core competency for SRE certifications like AWS DevOps or Google SRE.

62. How do you deploy to Azure?

• Define pipelines in azure-pipelines.yml for automation.
• Store credentials in Azure DevOps variables.
• Deploy to AKS with kubectl apply.
• Monitor with Azure Monitor for insights.
• Rollback with kubectl rollout undo.

This ensures reliable deployments, aligning with Azure DevOps SRE certification requirements.

63. Why use serverless for workloads?

Serverless reduces operational overhead for event-driven tasks. Deploy with AWS Lambda or Azure Functions, monitor with CloudWatch, and trigger via API Gateway. Validate with sam local. This ensures cost-effective, scalable CI/CD pipelines, a focus for SRE certifications in cloud-native environments.

64. When do you use multi-cloud strategies?

Use multi-cloud to avoid vendor lock-in and enhance resilience. Configure with Terraform, deploy with kubectl across AWS and Azure, and monitor with Prometheus. Validate with terraform plan. This ensures fault tolerance, a key focus for SRE certifications requiring robust cloud strategies.

65. Where do you store cloud credentials?

• Store in GitLab or Azure DevOps variables with encryption.
• Use HashiCorp Vault for secure access.
• Restrict with IAM roles in AWS or Azure.
• Validate with aws sts get-caller-identity.

Secure storage ensures compliance, critical for SRE certifications in secure cloud deployments.

66. Who manages cloud cost optimization?

SREs optimize costs with AWS Cost Explorer or Azure Cost Management. Use terraform plan for efficiency, select spot instances, and monitor with CloudWatch. Autoscaling with Kubernetes minimizes waste, ensuring cost-effective CI/CD pipelines, a key skill for SRE certifications in cloud environments.

67. Which cloud platforms support SRE?

• AWS supports EKS and ECS for containers.
• Azure offers AKS and Azure Monitor.
• GCP provides GKE and Stackdriver.
• Terraform automates across all platforms.

Monitor with Prometheus and deploy with kubectl, ensuring reliable CI/CD pipelines for SRE certifications.

68. How do you optimize cloud resources?

Use Kubernetes HPA for scaling, AWS spot instances for cost savings, and CloudWatch for monitoring. Validate with terraform plan and track costs with Cost Explorer.

Lightweight Docker images reduce overhead, ensuring cost-effective CI/CD pipelines. This approach minimizes waste, aligning with SRE certification requirements for efficient cloud-native systems.

69. What ensures cloud deployment consistency?

Infrastructure as Code (IaC) with Terraform ensures consistency. Define .tf files, execute with terraform apply, and validate with terraform plan. Monitor with Prometheus and deploy with kubectl. This reduces configuration drift, ensuring reliable CI/CD pipelines, a critical focus for SRE certifications in cloud-native environments.

70. Why use managed Kubernetes services?

Managed Kubernetes like AWS EKS or Azure AKS reduces operational overhead. Configure with kubectl, monitor with Prometheus, and validate with terraform plan. This ensures scalability and reliability, freeing SREs for strategic tasks, a key competency for certifications in cloud-native environments.

71. When do you migrate cloud platforms?

Migrate to optimize costs, performance, or compliance. Plan with Terraform, test with kubectl in staging, and monitor with CloudWatch. Document in Confluence and validate with terraform plan. This ensures seamless transitions, a critical skill for SRE certifications in multi-cloud environments.

72. Where do you store cloud configurations?

• Store in GitLab for version control.
• Use .tf files for Terraform definitions.
• Secure variables in GitLab CI/CD.
• Validate with terraform plan.

This ensures consistent, traceable configurations for CI/CD pipelines, vital for SRE certifications.

73. Who validates cloud deployments?

SREs validate deployments with developers. Use terraform plan for consistency, monitor with Prometheus, and deploy with kubectl. Document in Confluence and communicate via Slack. This ensures reliability and compliance, a key focus for SRE certifications in cloud-native systems.

74. Which tools optimize cloud performance?

• Kubernetes HPA scales dynamically with kubectl.
• CloudWatch monitors cloud-specific metrics.
• Terraform provisions efficient infrastructure.
• Grafana visualizes performance trends.

This ensures optimized CI/CD pipelines, critical for SRE certifications in cloud environments.

75. How do you handle cloud outages?

Analyze with CloudWatch logs, rollback with kubectl rollout undo, and notify via Slack. Implement fixes with terraform apply and monitor with Prometheus. Document in Confluence to prevent recurrence. This minimizes downtime, ensuring reliable CI/CD pipelines, a critical skill for SRE certifications in cloud-native systems.

Chaos Engineering

76. What is chaos engineering?

Chaos engineering tests system resilience by introducing failures. Use Chaos Monkey, define experiments in .gitlab-ci.yml, and monitor with Prometheus. Execute with kubectl to simulate pod failures and analyze in Grafana. This ensures robust CI/CD pipelines, a key focus for SRE certifications in cloud-native reliability.

77. How do you design chaos experiments?

• Define experiments in .gitlab-ci.yml with Litmus.
• Simulate failures with kubectl delete pod.
• Monitor with Prometheus and Grafana.
• Document results in Confluence.
• Validate scope with chaosctl.

This ensures systems withstand failures, aligning with SRE certification goals for resilient cloud-native systems.

78. Why perform chaos engineering?

Chaos engineering proactively identifies weaknesses. Use Chaos Monkey for terminations, monitor with Prometheus, and analyze with Grafana. Execute with kubectl and document in Confluence. This prevents outages, ensuring robust CI/CD pipelines, a critical skill for SRE certifications.

79. When do you schedule chaos experiments?

Schedule experiments during low-traffic periods to minimize impact. Use .gitlab-ci.yml, execute with kubectl, and monitor with Prometheus. Document in Confluence and validate with chaosctl. This ensures safe testing, a key requirement for SRE certifications in cloud-native or Kubernetes environments.

80. Where do you store chaos results?

• Store in Confluence for team access.
• Log metrics in Prometheus for analysis.
• Visualize in Grafana dashboards.
• Archive logs in ELK for traceability.

Results guide improvements, ensuring reliable CI/CD pipelines for SRE certifications.

81. Who conducts chaos experiments?

SREs conduct experiments, collaborating with developers. Use Chaos Monkey, execute with kubectl, and monitor with Prometheus. Document in Confluence and communicate via Slack. This ensures alignment, a key skill for SRE certifications in cloud-native or Kubernetes environments.

82. Which tools support chaos engineering?

• Chaos Monkey simulates instance terminations.
• Litmus provides Kubernetes-native chaos workflows.
• Gremlin injects controlled failures.
• Prometheus monitors experiment impacts.

Integrate with kubectl and Grafana, ensuring robust CI/CD pipelines for SRE certifications.

83. How do you measure chaos experiment success?

Measure success by validating SLO compliance post-experiment. Monitor with Prometheus, analyze with Grafana, and log in ELK.

Execute with kubectl and document in Confluence. Success means minimal impact, a critical focus for SRE certifications in resilient cloud-native systems.

Capacity Planning

84. What is capacity planning?

Capacity planning predicts resource needs to maintain performance. Use Prometheus for usage data, Grafana for trends, and Terraform for provisioning. Validate with terraform plan and scale with kubectl. This prevents bottlenecks, ensuring scalable CI/CD pipelines, a core competency for SRE certifications in cloud-native systems.

85. How do you forecast resource demands?

• Analyze metrics with Prometheus for trends.
• Predict growth using Grafana dashboards.
• Model capacity with Kubernetes HPA.
• Validate with terraform plan.
• Monitor with CloudWatch for adjustments.

This ensures sufficient resources, critical for CI/CD pipelines in SRE certifications.

86. Why is capacity planning critical?

Capacity planning prevents performance degradation. Monitor with Prometheus, forecast with Grafana, and provision with Terraform. Scale with kubectl and validate with terraform plan. This ensures scalability, a core competency for SRE certifications in cloud-native or high-traffic systems.

87. When do you adjust capacity?

Adjust capacity when metrics show high utilization or SLO breaches. Monitor with Prometheus, scale with kubectl, and provision with terraform apply. Analyze with Grafana and validate with terraform plan. This prevents outages, critical for SRE certifications in reliable systems.

88. Where do you analyze capacity trends?

• Grafana visualizes CPU and memory trends.
• Prometheus collects raw metrics.
• CloudWatch provides cloud-specific insights.
• ELK correlates logs with metrics.

Access via Grafana or Kibana, ensuring proactive planning for SRE certifications.

89. Who performs capacity planning?

SREs perform planning, collaborating with ops teams. Use Prometheus, Grafana, and Terraform. Scale with kubectl and validate with terraform plan. This ensures sufficient resources, a key focus for SRE certifications in cloud-native environments.

90. Which metrics guide capacity planning?

• CPU and memory usage indicate constraints.
• Latency highlights performance bottlenecks.
• Traffic volume predicts scaling needs.
• Error rates signal capacity issues.

Collect with Prometheus and Grafana, ensuring scalable CI/CD pipelines for SRE certifications.

91. How do you optimize resource allocation?

Use Kubernetes HPA for dynamic scaling and AWS spot instances for cost savings. Validate with terraform plan and monitor with Prometheus.

Lightweight Docker images reduce overhead, ensuring cost-effective CI/CD pipelines. This approach minimizes waste, aligning with SRE certification requirements for efficient cloud-native systems.

Security and Compliance

92. What secures sensitive data in pipelines?

Secure data with HashiCorp Vault using vault commands. Store secrets in GitLab variables with masking, validate with gitlab-ci lint, and monitor with Prometheus.

Document in Confluence for traceability. This ensures compliance, reducing risks in CI/CD pipelines, a critical focus for SRE certifications in secure deployments.

93. How do you implement SAST in pipelines?

• Enable GitLab SAST in .gitlab-ci.yml.
• Scan code during CI/CD execution.
• Review reports in GitLab Security tab.
• Integrate Snyk for deeper scans.
• Monitor with Prometheus for trends.

This ensures secure code, a key focus for SRE certifications in DevSecOps pipelines.

94. Why scan for vulnerabilities in CI/CD?

Scanning detects flaws early, reducing risks. Enable SAST in .gitlab-ci.yml, review with GitLab, and integrate Snyk. Monitor with Prometheus and document in Confluence. This ensures secure CI/CD pipelines, essential for SRE certifications in cloud-native systems.

95. When do you enforce compliance policies?

Enforce policies during regulated deployments. Configure mandatory jobs in .gitlab-ci.yml, set approvals in GitLab, and monitor with audit logs. Validate with gitlab-ci lint and track in Confluence. This ensures adherence to standards, critical for SRE certifications in regulated industries.

96. Where do you store security scan results?

• Store in GitLab Security & Compliance tab.
• Archive in Confluence for audits.
• Log metrics in Prometheus for trends.
• Centralize data in ELK for searchability.

Access via GitLab or Kibana, ensuring traceability for SRE certifications.

97. Who defines security policies?

SREs and compliance officers define policies in GitLab or Confluence. Configure scans in .gitlab-ci.yml, validate with gitlab-ci lint, and monitor with Prometheus. Collaborate via Slack to align teams. This ensures secure CI/CD pipelines, vital for SRE certifications in regulated environments.

98. Which tools enhance pipeline security?

• HashiCorp Vault manages secrets securely.
• Snyk scans code for vulnerabilities.
• GitLab SAST detects pipeline issues.
• Prometheus monitors security metrics.

Integrate with kubectl and validate with gitlab-ci lint, ensuring secure CI/CD pipelines for SRE certifications.

99. How do you prepare for compliance audits?

Prepare with ELK logs, Confluence documentation, and SAST in .gitlab-ci.yml. Monitor with Prometheus, validate with gitlab-ci lint, and restrict access with IAM.

This ensures traceability, a key focus for SRE certifications in regulated industries. Audit-ready documentation and automated scans streamline compliance processes, ensuring robust CI/CD pipelines.

Performance Optimization

100. What optimizes application performance?

Optimize with Redis caching, NGINX load balancing, and kubectl scaling. Monitor with Prometheus, analyze with Grafana, and validate with unit tests.

Lightweight Docker images reduce overhead, ensuring fast CI/CD pipelines. This approach minimizes latency, aligning with SRE certification requirements for high-traffic cloud-native systems.

101. How do you reduce system latency?

• Cache queries with Redis for speed.
• Optimize database queries with psql EXPLAIN.
• Use Cloudflare CDN for network efficiency.
• Monitor with Prometheus for insights.
• Scale with kubectl in Kubernetes.

This ensures low-latency CI/CD pipelines, vital for SRE certifications.

102. Why monitor application performance?

Monitoring identifies bottlenecks, ensuring SLO compliance. Use Prometheus for metrics, Grafana for visualization, and ELK for logs. Validate fixes with kubectl and document in Confluence. This prevents degradation, a core competency for SRE certifications in cloud-native systems.

103. When do you optimize resource usage?

Optimize when metrics show high CPU or memory usage. Use kubectl top pods, scale with Kubernetes HPA, and monitor with Prometheus. Validate with terraform plan. This ensures cost-effective CI/CD pipelines, critical for SRE certifications in high-traffic cloud-native environments.