100+ SRE Interview Questions and Answers [2025 Edition]

Site Reliability Engineering (SRE) blends software engineering with operations to ensure scalable, reliable systems. This 2025 guide provides 104 scenario-based interview questions, covering system design, incident management, automation, and monitoring. Designed for DevOps professionals, it offers practical solutions to optimize reliability, troubleshoot incidents, and manage cloud-native deployments, preparing candidates for technical interviews at top companies like Google, Amazon, and Netflix.

System Design and Scalability

1. What defines a reliable system in SRE?

A reliable system consistently performs as intended, minimizing downtime and errors. SREs ensure reliability through robust design, monitoring, and automation. Define Service Level Indicators (SLIs) like uptime, set Service Level Objectives (SLOs) for 99.9% availability, and monitor with Prometheus. Use kubectl to manage Kubernetes clusters and automate scaling with Terraform. This approach ensures system stability and scalability in cloud-native environments, supporting business-critical applications.

2. How do you design a scalable web application?

• Break application into microservices for independent scaling.
• Use load balancers like NGINX to distribute traffic evenly.
• Implement horizontal scaling with Kubernetes for dynamic resource allocation.
• Cache data with Redis to reduce database load.
• Monitor performance with Prometheus and Grafana for insights.
• Validate configurations with terraform plan for consistency.
  This ensures high availability, handles traffic spikes, and supports cloud-native deployments with minimal latency.

3. Why choose horizontal over vertical scaling?

Horizontal scaling adds more machines, offering better fault tolerance and flexibility than vertical scaling, which increases single-machine resources. Configure Kubernetes with kubectl to scale pods dynamically and monitor with Prometheus. Horizontal scaling reduces single points of failure, supports distributed systems, and is cost-effective in cloud environments like AWS, ensuring reliable performance for high-traffic applications in production deployments.

4. When do you use load balancing in system design?

Use load balancing to distribute traffic across servers, ensuring high availability. Configure NGINX or AWS ELB in deployments, monitor with Datadog, and validate with curl to test endpoints. Load balancing prevents server overload, supports scalability, and enhances reliability in cloud-native systems, especially for microservices or high-traffic applications requiring consistent performance and minimal downtime.

5. Where do you store application state?

• Use databases like PostgreSQL for persistent data storage.
• Store session data in Redis for fast, in-memory access.
• Manage state in Kubernetes with StatefulSets for consistency.
• Monitor state integrity with Prometheus for reliability insights.
  This ensures data availability, supports scalability, and prevents data loss in distributed systems, enabling robust state management in cloud-native or microservices-based applications.

6. Who designs fault-tolerant systems in SRE?

SREs design fault-tolerant systems by implementing redundancy, automation, and monitoring. Use Terraform to provision redundant infrastructure, configure Kubernetes for auto-recovery with kubectl, and monitor with Grafana. This minimizes downtime, ensures quick recovery from failures, and maintains service reliability in cloud-native environments, supporting high-availability applications with robust error-handling mechanisms.

7. Which tools support scalable system design?

• Kubernetes orchestrates containers for dynamic scaling.
• Terraform automates infrastructure provisioning across clouds.
• Prometheus monitors system performance and alerts.
• NGINX handles load balancing for traffic distribution.
  Tools integrate via terraform apply and kubectl, ensuring scalability and reliability in CI/CD pipelines. Monitor with Grafana to optimize resource usage, supporting cloud-native deployments for high-traffic applications.

8. How do you optimize database performance?

• Index frequently queried columns with psql for faster retrieval.
• Use connection pooling with PgBouncer to reduce overhead.
• Partition large tables to improve query performance.
• Monitor with Prometheus to identify bottlenecks.
• Cache queries with Redis to minimize database load.
  Validate with EXPLAIN in PostgreSQL to ensure efficient queries, enhancing database performance in cloud-native applications.

Incident Management

9. What steps resolve a production outage?

To resolve a production outage, assess the issue using logs from journalctl -u service, rollback with kubectl rollout undo, and notify teams via Slack. Identify root causes with Prometheus metrics, implement fixes, and document in postmortems. This restores service quickly, minimizes downtime, and prevents recurrence in cloud-native or Kubernetes environments, ensuring reliable incident response for critical systems.

10. How do you handle on-call emergencies?

• Assess incidents using logs from journalctl or kubectl logs.
• Prioritize based on impact to SLOs and user experience.
• Escalate to specialized teams via PagerDuty if needed.
• Implement known fixes or rollbacks with kubectl rollout undo.
• Document actions in postmortems for future prevention.
  Communicate via Slack and monitor with Prometheus to ensure quick resolution in CI/CD pipelines.

11. Why conduct postmortems after incidents?

Postmortems identify root causes, prevent recurrence, and improve reliability. Review logs with journalctl, analyze metrics with Prometheus, and document findings in Confluence. Engage teams via Slack to collaborate on fixes, implement with kubectl or Terraform, and monitor outcomes with Grafana. This ensures continuous improvement, enhancing system resilience in cloud-native or Kubernetes-based production environments.

12. When do you escalate incidents?

Escalate incidents when they exceed team expertise or impact SLOs significantly. Use PagerDuty for escalation, monitor with Prometheus, and communicate via Slack. Document escalations in postmortems and validate fixes with kubectl apply. This ensures rapid resolution, minimizes downtime, and maintains reliability in CI/CD pipelines for cloud-native or high-availability systems, preventing prolonged outages.

13. Where do you store incident logs?

• Store logs in ELK stack for centralized analysis.
• Access system logs via journalctl -u service.
• Use CloudWatch for AWS-based application logs.
• Monitor log integrity with Prometheus for reliability.
  Logs are accessible via Kibana, ensuring traceability and quick debugging in CI/CD pipelines, supporting efficient incident management in cloud-native or Kubernetes environments.

14. Who leads incident response in SRE?

SREs with on-call responsibilities lead incident response, coordinating with developers and ops teams. Use PagerDuty for scheduling, monitor with Prometheus, and communicate via Slack. Implement fixes with kubectl or Terraform and document in Confluence. This ensures rapid, organized resolution, maintaining system reliability in cloud-native or Kubernetes-based CI/CD pipelines for critical applications.

15. Which metrics guide incident resolution?

• Monitor latency and error rates with Prometheus for insights.
• Track SLO compliance to prioritize critical issues.
• Analyze resource usage with Grafana for bottlenecks.
• Check log patterns in ELK for root cause analysis.
  Metrics inform decisions, enabling quick fixes with kubectl or Terraform, ensuring minimal downtime in CI/CD pipelines for cloud-native or high-traffic applications.

16. How do you reduce mean time to resolution (MTTR)?

Reduce MTTR by automating alerts with Prometheus, centralizing logs in ELK, and using runbooks in Confluence. Implement fixes with kubectl rollout or Terraform, monitor with Grafana, and validate with unit tests. Clear communication via Slack ensures team alignment, minimizing downtime in CI/CD pipelines for cloud-native or Kubernetes environments, enhancing reliability and incident response efficiency.

Automation and CI/CD

17. What automates repetitive SRE tasks?

Ansible and Terraform automate tasks like provisioning and configuration. Define playbooks in Ansible or use terraform apply for infrastructure, validate with ansible-playbook --check, and monitor with Prometheus. Automation reduces manual errors, speeds up deployments, and ensures consistency in CI/CD pipelines, supporting scalable and reliable cloud-native or Kubernetes-based systems for high-availability applications.

18. How do you configure CI/CD pipelines?

• Define pipelines in .gitlab-ci.yml or Jenkinsfile for automation.
• Use Docker containers for isolated job execution.
• Integrate with Kubernetes via kubectl for deployments.
• Monitor pipeline health with Prometheus and Grafana.
• Validate configurations with gitlab-ci lint or Jenkins CLI.
  This ensures automated builds, tests, and deployments, enhancing reliability in cloud-native CI/CD workflows.

19. Why automate infrastructure provisioning?

Automation with Terraform or Ansible ensures consistent, repeatable infrastructure setups. Execute with terraform apply or ansible-playbook, validate with terraform plan, and monitor with Prometheus. This reduces manual errors, speeds up deployments, and supports scalability in CI/CD pipelines, enabling reliable cloud-native or Kubernetes environments for microservices or high-traffic applications, ensuring compliance and efficiency.

20. When do you use blue-green deployments?

Use blue-green deployments for zero-downtime updates in production. Configure with kubectl apply in Kubernetes, switch traffic with NGINX, and monitor with Prometheus. Rollback with kubectl rollout undo if issues arise. This ensures seamless updates, maintaining reliability in CI/CD pipelines for cloud-native or high-availability systems, minimizing user impact during deployments.

21. Where do you store pipeline configurations?

• Store in .gitlab-ci.yml or Jenkinsfile in Git repositories.
• Use GitLab’s Settings > CI/CD for variables.
• Secure secrets with HashiCorp Vault for compliance.
• Validate configurations with gitlab-ci lint or Jenkins CLI.
  Centralized storage ensures version control, enabling reliable CI/CD pipelines in cloud-native or Kubernetes environments for consistent and secure deployments.

22. Who manages CI/CD pipeline security?

SREs and DevOps engineers secure pipelines by setting protected variables in GitLab or Jenkins, using vault for secrets, and enabling SAST scans. Monitor with Prometheus and validate with gitlab-ci lint. This prevents leaks, ensures compliance, and maintains secure CI/CD workflows in cloud-native or Kubernetes pipelines, protecting sensitive data and deployments.

23. Which tools enhance CI/CD automation?

• GitLab automates pipelines with .gitlab-ci.yml configurations.
• Jenkins orchestrates complex workflows with plugins.
• Terraform provisions infrastructure for consistent deployments.
• Docker ensures isolated, reproducible environments.
  Integrate with kubectl for Kubernetes and monitor with Prometheus, ensuring scalable, reliable CI/CD pipelines in cloud-native environments for efficient software delivery.

24. How do you optimize pipeline performance?

Optimize pipelines by caching dependencies in .gitlab-ci.yml, using parallel jobs, and scaling runners with Kubernetes. Monitor with Prometheus, validate with gitlab-ci lint, and analyze with Grafana. Lightweight Docker images reduce overhead, ensuring fast, reliable CI/CD workflows in cloud-native or Kubernetes environments, minimizing build times and supporting high-throughput deployments.

Monitoring and Observability

25. What monitors system health in SRE?

Prometheus and Grafana monitor system health, tracking metrics like CPU usage and latency. Configure prometheus.yml, visualize with Grafana dashboards, and set alerts with promtool. Centralize logs with ELK and access via journalctl -u service. This ensures proactive issue detection, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes-based systems.

26. How do you set up alerting for outages?

• Configure Prometheus alerts in prometheus.yml for thresholds.
• Integrate with PagerDuty for real-time notifications.
• Test alerts with promtool test rules for accuracy.
• Monitor alert triggers in Grafana for quick response.
• Centralize logs in ELK for incident analysis.
  This ensures rapid outage detection, minimizing downtime in cloud-native CI/CD pipelines.

27. Why is observability critical for SRE?

Observability identifies issues before they impact users, using metrics, logs, and traces. Integrate Prometheus for metrics, ELK for logs, and Jaeger for tracing. Visualize with Grafana and set alerts with promtool. This ensures proactive issue resolution, enhancing reliability in CI/CD pipelines for cloud-native or Kubernetes environments, supporting high-availability systems.

28. When do you analyze system logs?

Analyze logs during incidents or performance issues to identify root causes. Access with journalctl -u service or Kibana, centralize with ELK, and monitor with Prometheus. Correlate logs with metrics in Grafana to pinpoint issues. This ensures quick debugging, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes-based systems.

29. Where do you visualize system metrics?

• Grafana dashboards display metrics like latency and CPU usage.
• Prometheus collects raw metrics for real-time monitoring.
• ELK stack visualizes logs alongside metrics for correlation.
• CloudWatch provides AWS-specific metric visualizations.
  Access via Grafana or Kibana, ensuring comprehensive observability in CI/CD pipelines for cloud-native or Kubernetes environments, enhancing system reliability.

30. Who monitors production systems?

SREs monitor production systems using Prometheus, Grafana, and ELK. Set alerts with promtool, access logs with journalctl, and visualize metrics in Grafana. Collaborate via Slack for real-time updates. This ensures proactive issue detection, maintaining uptime and reliability in CI/CD pipelines for cloud-native or Kubernetes-based high-availability applications.

31. Which metrics track system reliability?

• Uptime tracks overall system availability and SLO compliance.
• Error rates identify application or infrastructure issues.
• Latency measures response times for user experience.
• Resource usage monitors CPU and memory constraints.
  Collect with Prometheus, visualize with Grafana, and analyze with CloudWatch, ensuring reliable CI/CD pipelines in cloud-native or Kubernetes environments.

32. How do you reduce monitoring overhead?

Reduce overhead by filtering metrics in prometheus.yml, using lightweight agents like Telegraf, and aggregating logs with ELK. Visualize with Grafana, validate with promtool, and monitor resource usage with kubectl top pods. This minimizes costs while ensuring effective observability in CI/CD pipelines for cloud-native or Kubernetes environments, maintaining system reliability.

Cloud Technologies

33. What provisions cloud infrastructure in SRE?

Terraform provisions cloud infrastructure with consistent, repeatable setups. Define resources in .tf files, execute with terraform apply, and validate with terraform plan. Monitor with CloudWatch and integrate with Kubernetes via kubectl. This ensures scalable, reliable infrastructure in CI/CD pipelines for AWS, Azure, or GCP, supporting cloud-native deployments with minimal manual intervention.

34. How do you deploy to AWS with SRE principles?

• Define deployments in .gitlab-ci.yml with aws cli commands.
• Store credentials in GitLab variables for security.
• Execute with aws deploy for ECS or EKS clusters.
• Monitor with CloudWatch for real-time insights.
• Rollback with aws rollback if issues arise.
  This ensures reliable, automated deployments in AWS, supporting cloud-native CI/CD pipelines.

35. Why use Kubernetes in cloud environments?

Kubernetes enables scalable, fault-tolerant deployments with automated scaling. Configure with kubectl, monitor with Prometheus, and manage pods with StatefulSets. It supports microservices, reduces downtime, and integrates with CI/CD pipelines via GitLab or Jenkins, ensuring reliable cloud-native deployments in AWS, Azure, or GCP for high-availability applications with minimal operational overhead.

36. When do you use serverless architectures?

Use serverless for event-driven workloads to reduce operational overhead. Deploy with AWS Lambda or Azure Functions, monitor with CloudWatch, and trigger via API Gateway. Validate with sam local for testing. This minimizes infrastructure management, ensuring cost-effective, scalable CI/CD pipelines in cloud-native environments for applications with variable traffic patterns.

37. Where do you store cloud credentials?

• Store in GitLab or Jenkins CI/CD variables with encryption.
• Use HashiCorp Vault with vault commands for secure access.
• Restrict access with IAM roles in AWS or Azure.
• Validate access with aws sts get-caller-identity or equivalent.
  Secure storage prevents leaks, ensuring compliant CI/CD pipelines in cloud-native or Kubernetes environments.

38. Who manages cloud resource costs?

SREs manage costs using tools like AWS Cost Explorer or Azure Cost Management. Optimize with terraform plan, use spot instances, and monitor with CloudWatch. Autoscaling with Kubernetes reduces waste, ensuring cost-effective CI/CD pipelines. Regular audits in Grafana dashboards maintain budget compliance in cloud-native or multi-cloud environments for scalable deployments.

39. Which cloud platforms support SRE workflows?

• AWS supports EKS and ECS for containerized deployments.
• Azure provides AKS and robust monitoring with Azure Monitor.
• GCP offers GKE for Kubernetes and Stackdriver for observability.
• Terraform integrates with all for infrastructure automation.
  Monitor with Prometheus and deploy with kubectl, ensuring reliable CI/CD pipelines in cloud-native environments.

40. How do you optimize cloud resource usage?

Optimize by using autoscaling in Kubernetes, selecting spot instances in AWS, and monitoring with CloudWatch. Execute terraform plan to validate configurations and track costs with Cost Explorer. Lightweight containers with Docker reduce overhead, ensuring cost-effective, reliable CI/CD pipelines in cloud-native or multi-cloud environments for scalable, high-performance applications.

Chaos Engineering

41. What is chaos engineering in SRE?

Chaos engineering intentionally introduces failures to test system resilience. Use tools like Chaos Monkey, define experiments in .gitlab-ci.yml, and monitor with Prometheus. Execute with kubectl to simulate pod failures and analyze results in Grafana. This identifies weaknesses, ensuring robust CI/CD pipelines in cloud-native or Kubernetes environments, enhancing reliability for critical systems.

42. How do you implement chaos experiments?

• Define experiments in .gitlab-ci.yml with chaos tools like Litmus.
• Simulate failures with kubectl delete pod for Kubernetes.
• Monitor impacts with Prometheus and Grafana dashboards.
• Document results in Confluence for team insights.
• Validate experiment scope with chaosctl for accuracy.
  This ensures systems withstand failures, enhancing reliability in cloud-native CI/CD pipelines.

43. Why perform chaos engineering?

Chaos engineering proactively identifies weaknesses, improving system reliability. Use Chaos Monkey for random terminations, monitor with Prometheus, and analyze with Grafana. Execute experiments with kubectl in Kubernetes and document in Confluence. This prevents outages, ensuring robust CI/CD pipelines in cloud-native or high-availability systems, supporting scalable and fault-tolerant systems.

44. When do you run chaos experiments?

Run chaos experiments during low-traffic periods to minimize user impact. Schedule with .gitlab-ci.yml, execute with kubectl, and monitor with Prometheus. Document outcomes in Confluence and validate with chaosctl. This ensures safe testing, identifying vulnerabilities in CI/CD pipelines for cloud-native or Kubernetes environments, enhancing system resilience and reliability.

45. Where do you store chaos experiment results?

• Store in Confluence for team access and documentation.
• Log metrics in Prometheus for performance analysis.
• Visualize impacts in Grafana dashboards for insights.
• Archive logs in ELK for long-term traceability.
  Results guide improvements, ensuring robust CI/CD pipelines in cloud-native or Kubernetes environments with enhanced system reliability and fault tolerance.

46. Who conducts chaos engineering experiments?

SREs conduct chaos experiments, collaborating with developers. Use Chaos Monkey or Litmus, execute with kubectl, and monitor with Prometheus. Document in Confluence and communicate via Slack. This ensures team alignment, identifying system weaknesses in CI/CD pipelines for cloud-native or Kubernetes environments, enhancing resilience and reliability for critical applications.

47. Which tools support chaos engineering?

• Chaos Monkey simulates random instance terminations.
• Litmus provides Kubernetes-native chaos workflows.
• Gremlin offers controlled failure injection for testing.
• Prometheus monitors experiment impacts and metrics.
  Integrate with kubectl and visualize with Grafana, ensuring robust CI/CD pipelines in cloud-native environments, identifying weaknesses, and enhancing system reliability.

48. How do you measure chaos experiment success?

Measure success by validating SLO compliance post-experiment. Monitor metrics with Prometheus, analyze with Grafana, and log in ELK. Execute experiments with kubectl and document in Confluence. Success means minimal user impact and quick recovery, ensuring reliable CI/CD pipelines in cloud-native or Kubernetes environments, enhancing system resilience and fault tolerance.

Capacity Planning

49. What is capacity planning in SRE?

Capacity planning predicts resource needs to ensure system performance. Use Prometheus to monitor usage, forecast with Grafana trends, and provision with Terraform. Validate with terraform plan and scale with kubectl in Kubernetes. This prevents bottlenecks, ensuring scalable, reliable CI/CD pipelines in cloud-native or high-traffic environments, supporting business-critical applications.

50. How do you forecast resource needs?

• Analyze historical metrics with Prometheus for usage trends.
• Predict growth using Grafana dashboards for visualization.
• Model capacity with tools like Kubernetes HPA.
• Validate forecasts with terraform plan for accuracy.
• Monitor real-time usage with CloudWatch for adjustments.
  This ensures sufficient resources, maintaining reliability in CI/CD pipelines for cloud-native systems.

51. Why is capacity planning critical?

Capacity planning prevents performance degradation under load. Monitor with Prometheus, forecast with Grafana, and provision with Terraform. Scale with kubectl in Kubernetes and validate with terraform plan. This ensures scalability, minimizes downtime, and supports reliable CI/CD pipelines in cloud-native or high-traffic environments, meeting SLOs and user expectations.

52. When do you adjust resource capacity?

Adjust capacity when metrics show high utilization or SLO breaches. Monitor with Prometheus, scale with kubectl in Kubernetes, and provision with terraform apply. Analyze trends in Grafana and validate with terraform plan. This ensures performance, preventing outages in CI/CD pipelines for cloud-native or high-availability systems, maintaining reliability and user satisfaction.

53. Where do you analyze capacity trends?

• Grafana dashboards visualize CPU and memory trends.
• Prometheus collects raw metrics for analysis.
• CloudWatch provides cloud-specific usage insights.
• ELK correlates logs with capacity metrics.
  Access via Grafana or Kibana, ensuring proactive planning in CI/CD pipelines for cloud-native or Kubernetes environments, supporting scalable and reliable systems.

54. Who performs capacity planning?

SREs perform capacity planning, collaborating with ops teams. Use Prometheus for metrics, Grafana for visualization, and Terraform for provisioning. Scale with kubectl and validate with terraform plan. This ensures sufficient resources, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes environments, supporting high-availability applications with minimal performance issues.

55. Which metrics guide capacity planning?

• CPU and memory usage indicate resource constraints.
• Request latency highlights performance bottlenecks.
• Traffic volume predicts scaling needs.
• Error rates signal capacity-related issues.
  Collect with Prometheus, visualize with Grafana, and analyze with CloudWatch, ensuring scalable CI/CD pipelines in cloud-native or Kubernetes environments for reliable performance.

56. How do you optimize resource allocation?

Optimize by using Kubernetes HPA for autoscaling, selecting spot instances in AWS, and monitoring with Prometheus. Validate with terraform plan and track costs with Cost Explorer. Lightweight Docker images reduce overhead, ensuring cost-effective, reliable CI/CD pipelines in cloud-native or multi-cloud environments, supporting scalable and high-performance applications.

Coding and Algorithms

57. What algorithm finds the bottom-left node in a binary tree?

To find the bottom-left node, traverse recursively to the deepest leftmost leaf. Define a helper function in Python, track maximum height, and update the result. Use tree traversal with depth-first search, validate with unit tests, and monitor performance with Prometheus. This ensures efficient processing in CI/CD pipelines for cloud-native or Kubernetes-based applications requiring algorithmic solutions.

58. How do you implement a Fibonacci sequence generator?

• Use iterative approach in Python for efficiency.
• Store previous numbers in variables to calculate next.
• Validate output with unit tests for accuracy.
• Monitor performance with Prometheus for bottlenecks.
• Cache results with Redis for repeated calls.
  Deploy with .gitlab-ci.yml, ensuring reliable CI/CD pipelines in cloud-native environments for algorithmic tasks.

59. Why optimize algorithms for SRE tasks?

Optimized algorithms reduce resource usage and latency. Implement in Python or Go, test with pytest, and monitor with Prometheus. Cache results with Redis and validate with unit tests. This ensures efficient task execution, supporting scalable CI/CD pipelines in cloud-native or Kubernetes environments, enhancing system performance for critical applications.

60. When do you use multithreading in SRE?

Use multithreading for concurrent tasks like log processing. Implement in Python with threading, synchronize with mutexes, and monitor with Prometheus. Validate with unit tests and deploy with kubectl. This improves performance in CI/CD pipelines for cloud-native or Kubernetes environments, ensuring efficient handling of parallel tasks without deadlocks or resource contention.

61. Where do you store algorithm outputs?

• Store in PostgreSQL for persistent, structured data.
• Use Redis for temporary, high-speed caching.
• Archive in S3 for long-term storage and access.
• Monitor output integrity with Prometheus for reliability.
  Access via API or kubectl, ensuring traceability in CI/CD pipelines for cloud-native or Kubernetes environments, supporting efficient data management.

62. Who writes automation scripts in SRE?

SREs write automation scripts in Python or Go to streamline tasks. Store in GitLab, execute in .gitlab-ci.yml, and monitor with Prometheus. Validate with pylint and deploy with kubectl. This reduces manual effort, ensuring reliable CI/CD pipelines in cloud-native or Kubernetes environments, supporting scalable and efficient system operations for critical applications.

63. Which languages are preferred for SRE coding?

• Python excels for scripting and automation tasks.
• Go supports concurrent, high-performance systems.
• Bash handles quick system-level scripts.
• JavaScript automates web-related tasks in pipelines.
  Use pylint or gofmt for validation, monitor with Prometheus, ensuring efficient CI/CD pipelines in cloud-native or Kubernetes environments for reliable coding tasks.

64. How do you test algorithm performance?

Test performance with pytest for unit tests, profile with cProfile for bottlenecks, and monitor with Prometheus. Deploy with .gitlab-ci.yml and validate with gitlab-ci lint. Cache results with Redis to optimize repeated calls, ensuring efficient CI/CD pipelines in cloud-native or Kubernetes environments, supporting high-performance algorithmic solutions for critical systems.

Behavioral and Situational

65. What was a challenging incident you resolved?

In a past role, I resolved a database outage caused by a traffic surge. Analyzed logs with journalctl, scaled with kubectl scale, and notified teams via Slack. Implemented Redis caching and documented in Confluence. This restored service in 90 minutes, ensuring minimal downtime in a cloud-native CI/CD pipeline, improving system reliability and preventing recurrence.

66. How do you prioritize tasks during incidents?

• Assess impact on SLOs and user experience.
• Address critical outages affecting production first.
• Delegate low-priority tasks via Slack to teams.
• Monitor progress with Prometheus for real-time insights.
• Document decisions in Confluence for transparency.
  This ensures efficient incident resolution, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes environments.

67. Why collaborate with development teams?

Collaboration aligns SRE and development goals, improving system reliability. Share metrics via Grafana, coordinate via Slack, and document in Confluence. Implement fixes with kubectl and validate with unit tests. This reduces conflicts, ensures scalable CI/CD pipelines in cloud-native or Kubernetes environments, and supports high-availability systems with minimal downtime and errors.

68. When do you push back on feature requests?

Push back when features risk SLOs or stability. Analyze impact with Prometheus, discuss via Slack, and propose alternatives in Confluence. Validate with terraform plan or kubectl apply. This balances innovation and reliability, ensuring stable CI/CD pipelines in cloud-native or Kubernetes environments, preventing outages and maintaining user trust in high-traffic systems.

69. Where do you document incident learnings?

• Store in Confluence for team access and reference.
• Archive metrics in Prometheus for trend analysis.
• Log details in ELK for centralized searchability.
• Share summaries via Slack for team awareness.
  This ensures actionable insights, improving CI/CD pipelines in cloud-native or Kubernetes environments, enhancing system reliability and preventing incident recurrence.

70. Who do you report incidents to?

SREs report incidents to stakeholders, including managers and developers, via Slack or PagerDuty. Log details in Confluence, monitor with Prometheus, and validate fixes with kubectl. This ensures transparency, aligns teams, and maintains reliability in CI/CD pipelines for cloud-native or Kubernetes environments, supporting quick resolution and system stability for critical applications.

71. Which soft skills are critical for SREs?

• Communication ensures clear incident updates via Slack.
• Collaboration aligns teams for efficient problem-solving.
• Problem-solving identifies root causes with Prometheus.
• Adaptability handles unexpected outages with kubectl.
  Develop via training and practice, ensuring effective CI/CD pipelines in cloud-native or Kubernetes roles, supporting reliable and scalable system operations.

72. How do you handle stress during outages?

Manage stress by following runbooks in Confluence, prioritizing tasks with Prometheus metrics, and communicating via Slack. Implement fixes with kubectl and validate with unit tests. Regular training and chaos experiments with Chaos Monkey prepare for high-pressure scenarios, ensuring calm, effective incident resolution in CI/CD pipelines for cloud-native or Kubernetes environments.

Advanced SRE Scenarios

73. What handles a sudden traffic spike?

Handle spikes by scaling with kubectl scale in Kubernetes, caching with Redis, and load balancing with NGINX. Monitor with Prometheus, validate with terraform plan, and notify via Slack. This ensures system stability, minimizes latency, and maintains SLOs in CI/CD pipelines for cloud-native or high-traffic environments, preventing outages and ensuring reliability.

74. How do you migrate to a new cloud provider?

• Plan migration with Terraform for infrastructure consistency.
• Test deployments with kubectl in staging environments.
• Monitor performance with Prometheus and Grafana.
• Validate configurations with terraform plan for accuracy.
• Document steps in Confluence for team reference.
  This ensures seamless transitions, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes systems.

75. Why use Infrastructure as Code (IaC)?

IaC with Terraform ensures consistent, repeatable infrastructure. Execute with terraform apply, validate with terraform plan, and monitor with Prometheus. It reduces manual errors, supports scalability, and integrates with CI/CD pipelines via GitLab or Jenkins, ensuring reliable deployments in cloud-native or Kubernetes environments for microservices or high-availability systems.

76. When do you implement autoscaling?

Implement autoscaling when traffic varies or SLOs are at risk. Configure Kubernetes HPA with kubectl, monitor with Prometheus, and validate with terraform plan. Autoscaling ensures resource efficiency, minimizes costs, and maintains reliability in CI/CD pipelines for cloud-native or high-traffic environments, supporting dynamic scaling for microservices or critical applications.

77. Where do you store IaC configurations?

• Store in GitLab repositories for version control.
• Use .tf files for Terraform infrastructure definitions.
• Secure variables in GitLab CI/CD for access control.
• Validate with terraform plan for configuration accuracy.
  Access via GitLab UI, ensuring consistent CI/CD pipelines in cloud-native or Kubernetes environments, supporting reliable and scalable infrastructure management.

78. Who manages distributed system reliability?

SREs manage distributed system reliability using Kubernetes for orchestration, Prometheus for monitoring, and Terraform for provisioning. Coordinate via Slack, validate with kubectl, and document in Confluence. This ensures fault tolerance, scalability, and minimal downtime in CI/CD pipelines for cloud-native or microservices-based environments, supporting high-availability and resilient systems.

79. Which strategies ensure zero-downtime deployments?

• Use blue-green deployments with kubectl for seamless switches.
• Implement canary releases to test with small user groups.
• Monitor with Prometheus for real-time performance insights.
• Rollback with kubectl rollout undo if issues arise.
  These strategies minimize disruptions, ensuring reliable CI/CD pipelines in cloud-native or Kubernetes environments for critical applications.

80. How do you handle database migrations?

Plan migrations with Flyway, execute with flyway migrate, and validate with unit tests. Monitor with Prometheus, rollback with flyway undo if needed, and document in Confluence. This ensures data integrity, minimizes downtime, and supports reliable CI/CD pipelines in cloud-native or Kubernetes environments for applications requiring robust database management.

SRE Tools and Integrations

81. What integrates with GitLab for SRE workflows?

Prometheus, Grafana, and ELK integrate with GitLab for monitoring and observability. Configure in Settings > Integrations, validate with promtool, and visualize with Grafana. Use kubectl for Kubernetes deployments and journalctl for logs. This ensures robust CI/CD pipelines, enhancing reliability in cloud-native or Kubernetes environments for high-availability systems.

82. How do you use Terraform in SRE?

• Define infrastructure in .tf files for consistency.
• Execute with terraform apply for provisioning.
• Validate with terraform plan for accuracy.
• Monitor resources with Prometheus and CloudWatch.
• Store state in GitLab for version control.
  This automates infrastructure, ensuring scalable, reliable CI/CD pipelines in cloud-native or Kubernetes environments for efficient deployments.

83. Why use Prometheus for monitoring?

Prometheus provides real-time metrics for system health, alerting on SLO breaches. Configure in prometheus.yml, set alerts with promtool, and visualize with Grafana. Integrate with kubectl for Kubernetes and journalctl for logs. This ensures proactive issue detection, maintaining reliability in CI/CD pipelines for cloud-native or high-availability systems with minimal downtime.

84. When do you use Grafana for visualization?

Use Grafana when visualizing metrics like latency or CPU usage. Configure dashboards with Prometheus data, validate with promtool, and monitor via kubectl in Kubernetes. Share insights via Slack for team collaboration. This enhances observability, ensuring reliable CI/CD pipelines in cloud-native or Kubernetes environments, supporting proactive issue resolution and system performance.

85. Where do you configure monitoring tools?

• Configure Prometheus in prometheus.yml for metric scraping.
• Set up Grafana dashboards in Grafana UI for visualization.
• Store ELK configurations in logstash.yml for log aggregation.
• Manage integrations in GitLab’s Settings > Integrations.
  Validate with promtool and monitor via Kibana, ensuring robust CI/CD pipelines in cloud-native or Kubernetes environments.

86. Who sets up observability integrations?

SREs set up observability integrations with Prometheus, Grafana, and ELK. Configure in GitLab Settings, validate with promtool, and monitor with kubectl. Collaborate via Slack and document in Confluence. This ensures comprehensive monitoring, maintaining reliability in CI/CD pipelines for cloud-native or Kubernetes environments, supporting high-availability and scalable systems.

87. Which tools handle log aggregation?

• ELK stack centralizes logs for analysis and search.
• CloudWatch aggregates AWS-specific application logs.
• Fluentd collects and forwards logs to storage.
• Prometheus monitors log-related metrics for insights.
  Configure with logstash.yml, validate with journalctl, ensuring efficient CI/CD pipelines in cloud-native or Kubernetes environments for reliable log management.

88. How do you integrate Kubernetes with monitoring?

Integrate Kubernetes with Prometheus by configuring prometheus.yml to scrape pod metrics. Use kubectl to deploy monitoring agents, visualize with Grafana, and set alerts with promtool. Centralize logs with ELK and access via journalctl. This ensures comprehensive observability, maintaining reliable CI/CD pipelines in cloud-native or Kubernetes environments for high-availability systems.

Security and Compliance

89. What secures sensitive data in SRE pipelines?

Secure data with HashiCorp Vault, using vault commands for access. Store secrets in GitLab CI/CD variables, enable masked settings, and validate with gitlab-ci lint. Monitor access with Prometheus and document in Confluence. This prevents leaks, ensuring compliant CI/CD pipelines in cloud-native or Kubernetes environments, supporting secure deployments for critical applications.

90. How do you implement SAST in pipelines?

• Enable GitLab SAST in .gitlab-ci.yml with security/scan templates.
• Scan code for vulnerabilities during CI/CD execution.
• Review reports in GitLab’s Security & Compliance tab.
• Integrate Snyk for deeper vulnerability analysis.
• Monitor scan results with Prometheus for trends.
  This ensures secure code, maintaining reliable CI/CD pipelines in cloud-native or Kubernetes environments.

91. Why scan for vulnerabilities in CI/CD?

Scanning detects security flaws early, reducing production risks. Enable SAST in .gitlab-ci.yml, review with GitLab UI, and integrate Snyk for comprehensive scans. Monitor with Prometheus and document in Confluence. This ensures secure, compliant CI/CD pipelines in cloud-native or Kubernetes environments, protecting applications from exploits and maintaining system reliability.

92. When do you enforce compliance policies?

Enforce compliance during deployments to regulated environments. Configure mandatory jobs in .gitlab-ci.yml, set approval rules in GitLab Settings, and monitor with audit logs. Validate with gitlab-ci lint and track in Confluence. This ensures adherence to standards, supporting secure CI/CD pipelines in cloud-native or Kubernetes environments for industries like finance or healthcare.

93. Where do you store security scan results?

• Store in GitLab’s Security & Compliance tab for access.
• Archive in Confluence for team reference and audits.
• Log metrics in Prometheus for trend analysis.
• Centralize raw data in ELK for searchability.
  Access via GitLab UI or Kibana, ensuring traceable CI/CD pipelines in cloud-native or Kubernetes environments for compliance and security.

94. Who defines security policies in SRE?

SREs and compliance officers define policies in GitLab Settings or Confluence. Configure mandatory scans in .gitlab-ci.yml, validate with gitlab-ci lint, and monitor with Prometheus. Collaborate via Slack for alignment. This ensures secure, compliant CI/CD pipelines in cloud-native or Kubernetes environments, supporting regulated industries with robust security measures.

95. Which tools enhance pipeline security?

• HashiCorp Vault manages secrets with vault commands.
• Snyk scans for code and dependency vulnerabilities.
• GitLab SAST detects issues in .gitlab-ci.yml pipelines.
• Prometheus monitors security-related metrics and alerts.
  Integrate with kubectl and validate with gitlab-ci lint, ensuring secure CI/CD pipelines in cloud-native or Kubernetes environments for reliable deployments.

96. How do you handle compliance audits?

Prepare for audits by storing logs in ELK, documenting in Confluence, and enabling SAST in .gitlab-ci.yml. Monitor with Prometheus, validate with gitlab-ci lint, and restrict access with IAM roles. This ensures traceability, compliance with standards, and secure CI/CD pipelines in cloud-native or Kubernetes environments, supporting regulated industries like finance or healthcare.

Performance Optimization

97. What optimizes application performance?

Optimize by caching with Redis, load balancing with NGINX, and scaling with kubectl in Kubernetes. Monitor with Prometheus, analyze with Grafana, and validate with unit tests. Lightweight Docker images reduce overhead, ensuring fast, reliable CI/CD pipelines in cloud-native or Kubernetes environments, supporting high-traffic applications with minimal latency and optimal performance.

98. How do you reduce system latency?

• Cache frequent queries with Redis for faster access.
• Optimize database queries with psql EXPLAIN plans.
• Use CDN like Cloudflare to reduce network latency.
• Monitor with Prometheus for real-time insights.
• Scale with kubectl scale in Kubernetes for load handling.
  This ensures low-latency, reliable CI/CD pipelines in cloud-native or high-traffic environments.

99. 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 performance degradation, supporting reliable CI/CD pipelines in cloud-native or Kubernetes systems, ensuring high-availability systems meet user expectations with minimal downtime.

100. When do you optimize resource usage?

Optimize when metrics show high CPU or memory usage. Use kubectl top pods for insights, scale with Kubernetes HPA, and monitor with Prometheus. Validate with terraform plan and adjust with lightweight Docker images. This ensures cost-effective, reliable CI/CD pipelines in cloud-native or Kubernetes environments, supporting scalable and high-performance applications.

101. Where do you analyze performance metrics?

• Grafana dashboards visualize latency and resource usage.
• Prometheus collects raw metrics for real-time monitoring.
• CloudWatch provides cloud-specific performance insights.
• ELK correlates logs with metrics for analysis.
  Access via Grafana or Kibana, ensuring optimized CI/CD pipelines in cloud-native or Kubernetes environments for reliable and high-performance systems.

102. Who optimizes system performance?

SREs optimize performance using Prometheus for monitoring, Grafana for visualization, and kubectl for scaling. Collaborate via Slack, validate with unit tests, and document in Confluence. This ensures low latency, high availability, and cost-effective CI/CD pipelines in cloud-native or Kubernetes environments, supporting scalable and reliable systems for critical applications.

103. Which metrics guide performance optimization?

• Latency measures response times for user experience.
• CPU and memory usage indicate resource bottlenecks.
• Error rates highlight application or system issues.
• Throughput tracks request handling capacity.
  Collect with Prometheus, visualize with Grafana, and analyze with CloudWatch, ensuring optimized CI/CD pipelines in cloud-native or Kubernetes environments for reliability.

104. How do you handle performance bottlenecks?

Identify bottlenecks with Prometheus, analyze with Grafana, and optimize with Redis caching or kubectl scaling. Validate fixes with unit tests and document in Confluence. Lightweight Docker images reduce overhead, ensuring fast, reliable CI/CD pipelines in cloud-native or Kubernetes environments, minimizing latency and maintaining performance for high-traffic or critical applications.