Amazon Staff DevOps Engineer Interview Preparation Guide
Amazon's DevOps Engineer interview process for Staff level candidates typically consists of an initial recruiter screening, a technical phone screen focused on infrastructure and system design, and an onsite loop of 5 interviews covering advanced infrastructure system design, CI/CD pipeline architecture, Kubernetes and container orchestration mastery, production troubleshooting and incident response, and behavioral assessment with leadership principles evaluation. The process emphasizes practical ownership (building and running infrastructure), trade-off reasoning across cost, reliability, and scalability, and demonstrated impact at scale.
Interview Rounds
Recruiter Screening
What to Expect
Initial screening with Amazon recruiter to confirm background, experience level, and fit for Staff-level infrastructure role. Recruiter will discuss your infrastructure projects, team leadership experience, scale of systems managed, and career trajectory. This round validates that you meet the Staff level bar (12+ years experience, demonstrated domain expertise, cross-team or cross-functional leadership). Recruiter may ask about salary expectations, work authorization, availability, and reasons for job change.
Tips & Advice
Prepare a 2-3 minute introduction highlighting your most impactful infrastructure projects and the scale (number of microservices, traffic volume, teams managed). Emphasize concrete metrics: infrastructure cost savings, deployment frequency improvements, uptime achievements. For Staff level, focus on cross-team impact and strategic contributions rather than individual execution. Research Amazon's DevOps culture, infrastructure priorities (global scale, high reliability, cost optimization), and leadership principles. Have specific questions ready about the infrastructure team structure, their current challenges, and how they measure DevOps effectiveness.
Focus Topics
Cross-Functional Leadership and Ownership
Demonstrate experience leading infrastructure initiatives across development, operations, and security teams; driving adoption of standards (IaC, observability, deployment automation).
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Alignment with Amazon Leadership Principles
Prepare STAR stories mapping to 2-3 Amazon LPs most relevant to infrastructure work: Ownership (building and running systems), Bias for Action (rapid infrastructure decisions), Earn Trust (reliable infrastructure practices).
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Quantified Infrastructure Achievements
Prepare 3-4 stories with specific metrics: infrastructure cost reductions (%), deployment frequency (deployments/day), uptime improvements, automation scope (% of infrastructure managed via IaC).
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Career Trajectory and Impact Narrative
Articulate your 12+ year progression in infrastructure/DevOps, highlighting increasing scope (from single-server management to multi-region, multi-cloud orchestration) and impact (cost savings, reliability improvements, team leadership).
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Technical Phone Screen - Infrastructure System Design
What to Expect
60-minute live technical interview with a senior infrastructure engineer or DevOps manager. You will be asked to design infrastructure for a complex, large-scale application scenario (e.g., 'Design the infrastructure for a SaaS platform serving 100M+ daily active users globally'). You are expected to clarify requirements, propose architecture including compute, networking, storage, CI/CD, and monitoring, explain scaling strategies, discuss trade-offs (cost vs. availability, consistency vs. performance), estimate capacity, and handle follow-up questions. This screen tests strategic thinking, architectural depth, and your ability to communicate infrastructure decisions to experienced engineers.
Tips & Advice
Start by asking clarifying questions (traffic patterns, global presence, consistency requirements, cost constraints, team structure) before proposing architecture. Draw a high-level diagram mentally and describe services, communication patterns, and failure modes. For Staff level, go beyond basics: discuss multi-region strategies (latency vs. cost), edge caching, database sharding strategies, and multi-cloud considerations. Explicitly discuss observability from the ground up (what metrics matter, where do you instrument, how do you alert). Handle objections gracefully: if interviewer questions your choice, explain the trade-off, acknowledge constraints, and propose alternatives. Use real AWS/GCP/Azure services by name. Practice explaining why you rejected simpler alternatives (e.g., 'Single region is simpler but doesn't meet the 99.99% uptime requirement for global users'). Record yourself and review for clarity, filler words, and depth.
Focus Topics
Security and Compliance in Infrastructure Design
Integrate security into infrastructure design: network segmentation, secret management, identity and access (IAM), encryption (in-transit and at-rest), audit logging, compliance requirements (SOC2, HIPAA).
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Infrastructure as Code (IaC) and Deployment Strategy
Explain how you would manage infrastructure code (Terraform, CloudFormation, Helm) across teams, state management, testing of infrastructure changes, versioning, and safe deployment practices.
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Cost Optimization and Trade-Off Analysis
Demonstrate ability to estimate infrastructure costs, identify optimization opportunities (reserved instances, spot instances, auto-scaling policies), and make explicit trade-offs between cost and performance.
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Observability, Monitoring, and Alerting Architecture
Design monitoring and observability for large infrastructure: metrics collection strategy (Prometheus, CloudWatch), distributed tracing, log aggregation, alerting rules, dashboards, and incident runbooks.
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Large-Scale Multi-Region Infrastructure Architecture
Design highly available, globally distributed infrastructure handling 100M+ users. Include compute strategy (Kubernetes, auto-scaling), data replication across regions, failover mechanisms, and latency optimization.
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Onsite Round 1 - Advanced Infrastructure System Design
What to Expect
Deep-dive infrastructure architecture interview (60 minutes). Similar to phone screen but more comprehensive and with higher expectations. You will design a complex infrastructure scenario (possibly with real Amazon constraints: global availability, extreme scale, cost sensitivity, regulatory requirements). Interviewer will probe deeply into your design choices, asking follow-up questions to test flexibility, alternatives, and edge cases. You are expected to handle design pivots gracefully (e.g., 'What if we needed to support 10x more traffic?' or 'What if multi-region became a hard requirement?'). This round also evaluates your communication clarity, ability to draw architecture diagrams, and depth of cloud platform knowledge (AWS/GCP/Azure).
Tips & Advice
Spend the first 10 minutes on clarification: traffic volume, geographic distribution, availability requirements (SLA), consistency requirements, team size, deployment frequency. Propose a phased approach: start with a simple baseline architecture, then iterate toward production-ready design. Use whiteboard or digital drawing actively; don't just talk. For Staff level, expect to discuss architectural patterns (strangler pattern for migrations, circuit breakers, bulkheads). Be prepared to explain why you chose certain services: 'We use managed RDS instead of self-hosted because it reduces operational burden and we get automated backups and failover.' Discuss disaster recovery explicitly: RPO/RTO targets, backup strategies, and failover procedures. When interviewer challenges your design, don't become defensive; instead, acknowledge the constraint and explore trade-offs: 'That's a valid concern. If we must reduce costs, we could use spot instances for non-critical workloads, but we'd need to handle interruptions gracefully.' Practice thinking out loud while drawing.
Focus Topics
Multi-Cloud or Hybrid Infrastructure Strategy
Design infrastructure that spans AWS, GCP, Azure, or includes on-premises components. Discuss workload placement strategy, data consistency across clouds, network connectivity, and vendor lock-in mitigation.
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Infrastructure Automation and Orchestration Patterns
Design automation strategies for common infrastructure tasks: dynamic service discovery, load balancing, self-healing systems, chaos engineering, and progressive deployment patterns (canary, blue-green).
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Disaster Recovery Planning and Business Continuity
Design recovery strategies for infrastructure failures: define RPO (Recovery Point Objective) and RTO (Recovery Time Objective), design backup and restore procedures, plan for multi-region failover, document runbooks for common failure scenarios.
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Capacity Planning and Load Forecasting
Estimate infrastructure capacity needs based on traffic forecasts, apply safe headroom, plan for peak load, design auto-scaling policies, and forecast costs. Use real numbers (e.g., 500M requests/day, 10Tbps egress).
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Onsite Round 2 - CI/CD Pipeline Design and Deployment Architecture
What to Expect
Technical deep-dive on continuous integration and deployment (45-60 minutes). You will design a production CI/CD pipeline for a complex, multi-service organization (e.g., 100+ microservices, multiple deployment targets, global infrastructure). Expected topics: source control strategy, build automation, testing strategy (unit, integration, contract, performance), artifact management, deployment orchestration, rollback procedures, and feature flags. You will draw the pipeline architecture, explain how teams interact with it, discuss failure modes and recovery, and handle questions about GitOps, deployment safety, and operational observability. This round evaluates your ability to think operationally: how to ship code quickly while maintaining reliability.
Tips & Advice
Start by understanding the scenario: How many services? What's the deployment frequency expectation (daily, hourly)? What's the risk tolerance (can we afford 5-minute outages)? How do teams operate (centralized, autonomous)? Draw the pipeline end-to-end: code commit → build → test → artifact → staging → production. For Staff level, emphasize safety and speed simultaneously: fast feedback loops (tests run in parallel, 10-15 min total pipeline), automated rollback, feature flags for decoupling deployment from release, canary deployments, and production observability tied to deployment gates. Discuss how you prevent bad deployments: test coverage, staging environments that mirror production, traffic mirroring, and chaos testing. Explain operational patterns: how do developers interact with the pipeline, how do you manage secrets, how do you handle infrastructure changes alongside application changes. Be prepared to discuss trade-offs: centralized pipeline (easier to manage) vs. team-specific pipelines (more autonomy), comprehensive testing (slower feedback) vs. fast feedback with risk, and local development loop vs. pipeline-driven development.
Focus Topics
Infrastructure Changes and Configuration Management
Design how infrastructure changes flow through the pipeline: testing infrastructure changes, approvals for breaking changes, safe rollout of infrastructure updates, and synchronization with application deployments.
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Observability Integration in CI/CD
Integrate deployment observability: pre-deployment checks (infrastructure health), deployment tracking, post-deployment validation (error rates, latency, resource usage), and automated rollback triggers.
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Feature Flags and Progressive Deployment Patterns
Design feature flag infrastructure for decoupling deployment from release. Implement progressive deployment patterns: canary deployments (route 5% traffic initially), blue-green deployments, and ring-based deployments for staged rollout.
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Multi-Stage Deployment Pipeline Architecture
Design CI/CD pipeline with multiple stages: commit, build, test (unit/integration/contract), artifact storage, staging, canary deployment, gradual rollout, and production. Include safety gates and rollback mechanisms.
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Testing Strategy for Production Safety
Design comprehensive testing: unit tests, integration tests, contract tests (for microservices), performance tests, security scanning, and chaos engineering. Explain how testing gates prevent bad deployments.
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Onsite Round 3 - Kubernetes and Container Orchestration Mastery
What to Expect
Technical interview on Kubernetes and container orchestration (60 minutes). You will be asked to design a Kubernetes cluster architecture for a large, complex, multi-tenant environment (e.g., 1000+ pods, multiple namespaces, diverse workloads). Topics include cluster architecture (control plane redundancy, node provisioning), workload scheduling and resource management, networking (service mesh, ingress), storage (persistent volumes, StatefulSets), security (RBAC, network policies), and operational concerns (upgrades, monitoring, troubleshooting). You will be expected to handle advanced scenarios: scaling clusters across multiple regions, multi-cloud Kubernetes, GitOps-based cluster management, and complex networking scenarios. This round evaluates your operational expertise: can you design systems that teams can reliably operate?
Tips & Advice
Begin with requirements clarification: workload types (stateless microservices, stateful databases, batch jobs), scalability needs, multi-tenancy requirements, geographic distribution, compliance requirements. Design a production Kubernetes architecture: multiple availability zones, auto-scaling node groups, monitoring and logging stack (Prometheus, ELK), and a GitOps-based deployment model (ArgoCD or Flux). For Staff level, go deep: discuss cluster federation for multi-region, service mesh (Istio, Linkerd) for advanced traffic management, advanced networking (CNI plugins, network policies for security), and custom controllers for domain-specific automation. Handle complex scenarios: 'How would you upgrade a 1000-pod cluster with zero downtime?' (Pod disruption budgets, graceful termination, draining nodes). Discuss cost optimization: node autoscaling, spot instances, resource requests/limits enforcement. Be prepared to troubleshoot in your head: 'Pods are crash-looping. How would you diagnose?' (Check events, logs, resource limits, health checks). Mention observability: how you monitor Kubernetes itself (kube-apiserver, kubelet), how you correlate application and infrastructure metrics, and how you detect cluster health issues before they impact applications.
Focus Topics
Kubernetes Storage and State Management
Design storage architecture for Kubernetes: persistent volumes for databases, StatefulSets for stateful workloads, backup strategies, disaster recovery, and handling storage scaling.
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Cluster Lifecycle and Operational Excellence
Design processes for Kubernetes cluster upgrades, node maintenance, scaling strategies (horizontal and vertical), cost optimization, and monitoring cluster health (API server, kubelet, etcd).
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Kubernetes Security and Compliance
Design Kubernetes security: RBAC for least-privilege access, network policies for microsegmentation, pod security policies, secret management (Vault, sealed secrets), image scanning, and audit logging.
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Kubernetes Cluster Architecture for Multi-Tenant Production
Design multi-AZ Kubernetes cluster with redundant control plane, auto-scaling node groups, network policies for tenant isolation, RBAC for access control, and resource quotas for fairness.
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Service Mesh and Advanced Networking
Design service mesh architecture (Istio, Linkerd) for traffic management, reliability (circuit breakers, retries), security (mTLS, fine-grained authorization), and observability (distributed tracing).
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Onsite Round 4 - Troubleshooting, Incident Response, and Operational Mastery
What to Expect
Practical troubleshooting and incident response interview (60 minutes). You will be presented with infrastructure scenarios and incidents to investigate and resolve. Scenarios may include: service is unreachable, pods are crash-looping and not recovering, deployment is stuck in a rollout, database performance has degraded, or infrastructure alerts are firing. You may be given access to a simulated environment (logs, metrics, events) or asked to walk through your debugging approach verbally. The evaluation focuses on: systematic troubleshooting methodology (not random guessing), knowledge of diagnostic tools and techniques, clear communication during investigation, root cause analysis, and proposed solutions. This round tests operational maturity: have you dealt with production incidents and learned from them?
Tips & Advice
Approach troubleshooting systematically, not haphazardly. Start by understanding the symptom clearly: what exactly is broken, when did it start, what changed recently? Ask clarifying questions. Use a methodical approach: check logs (application, infrastructure, audit), examine metrics (CPU, memory, disk, network), review recent changes (deployments, infrastructure updates, configuration changes), and check dependencies. For Kubernetes troubleshooting: understand pod lifecycle (pending, running, terminating), use kubectl describe pod, kubectl logs, and kubectl events effectively. For network issues: understand DNS resolution, understand service discovery, use network debugging tools (tcpdump, curl, netcat). For performance issues: understand resource contention (CPU, memory, I/O), profile the service, check for noisy neighbors. Communicate your thinking: 'I see error rate increased 10 minutes ago. Let me check deployments.' Propose root causes and verify them: 'My hypothesis is that the database is overloaded because we deployed a new service with aggressive polling. Let me verify by checking database connections.' For Staff level, demonstrate not just technical troubleshooting but also incident management: who needs to be notified, what's the impact, what's the immediate mitigation, and what's the long-term fix. Discuss post-incident practices: blameless postmortems, addressing root causes, preventing recurrence.
Focus Topics
Performance Debugging and Optimization
Debug performance issues: identify bottlenecks (CPU, memory, I/O, network), use profiling tools, analyze metrics, understand resource contention, and propose optimization strategies.
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Database and Data Store Troubleshooting
Diagnose database performance issues: slow queries, connection pooling exhaustion, replication lag, disk space, and backup failures. Understand query analysis tools and optimization strategies.
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Incident Management and Post-Mortem Practices
Understand incident response process: severity classification, escalation, communication (status pages, stakeholder updates), mitigation vs. permanent fix, and blameless postmortem practices that focus on systems, not individuals.
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Kubernetes Troubleshooting Deep Dive
Master Kubernetes troubleshooting: understand pod lifecycle and states, use kubectl effectively (describe, logs, events, exec, port-forward), diagnose scheduler issues, networking problems, and persistent volume issues.
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Systematic Infrastructure Troubleshooting Methodology
Master a structured approach to troubleshooting: gather symptoms, list potential causes, design tests to eliminate hypotheses, and iterate. Know diagnostic tools: kubectl, aws cli, network tools (curl, netcat, tcpdump), log analysis, and metrics queries.
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Onsite Round 5 - Behavioral and Leadership Principles (Bar Raiser)
What to Expect
Behavioral interview with a Bar Raiser (60 minutes). The Bar Raiser is an experienced Amazon leader from outside your direct team who evaluates you against Amazon's bar and leadership principles. This interview focuses on your past experiences, decision-making process, leadership influence, and alignment with Amazon values. You will be asked behavioral questions using the STAR method (Situation, Task, Action, Result). Expected topics: How did you influence teams without direct authority? How did you handle a difficult stakeholder? How did you drive a major infrastructure initiative? How did you balance speed and quality? How did you fail and what did you learn? For Staff level, Bar Raiser questions probe deep: strategic thinking, influence across organizations, handling ambiguity, and demonstrated ownership of large, complex initiatives. The Bar Raiser is looking for long-term potential, principled decision-making, and strong alignment with Amazon culture.
Tips & Advice
Prepare 8-10 detailed STAR stories covering: a complex infrastructure initiative you led (what was the vision, how did you influence others, what was the outcome), a conflict with a stakeholder and how you resolved it, a time you failed and what you learned, a time you balanced competing priorities (speed vs. reliability, cost vs. performance), a time you earned trust through consistent delivery, and a time you simplified complexity. For Staff level, focus on strategic contributions: How did you influence infrastructure strategy across multiple teams? How did you drive adoption of new practices (IaC, observability, deployment automation)? Prepare to discuss your leadership philosophy, how you mentor others, and how you make principled decisions. Map stories to Amazon leadership principles (Ownership, Bias for Action, Earn Trust, Customer Obsession, Think Big, Learn and Be Curious). For each story, have specific metrics: 'We improved deployment safety, reducing production incidents by 40%,' not just 'We improved deployment.' Address failures directly: 'I chose the wrong technology for that project, which delayed us 6 months. I learned to validate assumptions earlier with prototypes.' Be authentic and humble. The Bar Raiser is evaluating your character and judgment, not perfection.
Focus Topics
Learning from Failure and Handling Ambiguity
Discuss a significant failure in your career: why it happened, what you learned, how you prevented recurrence, and how it shaped your approach to infrastructure decisions. Also share experience navigating ambiguous situations.
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Influence Without Authority and Cross-Functional Leadership
Share examples of driving infrastructure adoption across multiple teams without direct authority: how you built consensus, addressed objections, and scaled practices (e.g., IaC adoption, observability standards).
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Amazon Leadership Principle: Think Big and Bias for Action
Share stories of ambitious infrastructure initiatives you proposed and led: migrating infrastructure to improve scalability, building new automation to reduce toil, or adopting new technologies to unlock capabilities. Balance vision with pragmatic execution.
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Amazon Leadership Principle: Earn Trust and Frugality
Demonstrate reliability through consistent delivery: how you built confidence with teams through proven results, how you optimized costs without sacrificing reliability, and how you made principled trade-off decisions.
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Amazon Leadership Principle: Ownership
Demonstrate deep ownership of infrastructure systems: you take responsibility for end-to-end outcomes, proactively identify and solve problems, drive improvements beyond your direct scope, and are accountable for both success and failures.
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Frequently Asked DevOps Engineer Interview Questions
Sample Answer
# instrumentation.py (imported first)
from opentelemetry import trace, metrics
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.auto_instrumentation import auto_instrument
resource = Resource.create({"service.name": "my-service"})
trace.set_tracer_provider(TracerProvider(resource=resource))
span_exporter = OTLPSpanExporter(endpoint="otel-collector:4317", insecure=True)
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(span_exporter))
# auto-instrument common libs
auto_instrument()Sample Answer
Sample Answer
Sample Answer
Sample Answer
kubectl get pods -o wide -n prod
kubectl describe pod my-web-xxx -n prod
kubectl get events -n prod --sort-by=.metadata.creationTimestampkubectl get nodes -o wide
kubectl describe node ip-xxx | egrep "Labels:|Taints:"
kubectl get node ip-xxx --show-labelskubectl get deploy my-web -n prod -o yamlkubectl taint nodes <on-demand-node> lifecycle=on-demand:NoSchedulekubectl label node <node> spot=truekubectl apply -f deploy.yaml
kubectl rollout restart deploy/my-web -n prod
kubectl get pods -o wide -n prodSample Answer
Sample Answer
Sample Answer
Risk = w1*(CS) + w2*(HIR) + w3*(1-TCQ) + w4*(SC) + w5*(1-DF) + w6*(1-OM)
DRS = round( Risk / (w1+w2+...+w6) * 100 )Sample Answer
terraform init
terraform state pull > full.tfstate
# parse full.tfstate to build lists per-account (script using jq)
jq '.resources[].instances[] | {address: .address, attributes: .attributes}' full.tfstateterraform state pull > state-full.json
# extract only desired resources into state-part.json (use jq/script)terraform init -backend-config="bucket=account-a-terraform-state" \
-backend-config="key=env/terraform.tfstate" \
-backend-config="region=us-east-1"
# Do NOT migrate full state automatically; we will push the filtered state.terraform state push state-part.json# Using a controlled node with creds for original backend
terraform init -backend-config="bucket=central-terraform-state" ...
terraform state rm <each-address> # or batch via scriptaws s3 cp s3://central-terraform-state/env/terraform.tfstate ./backup-central-$(date +%s).tfstate
aws s3 cp s3://account-a-terraform-state/env/terraform.tfstate ./backup-account-a-$(date +%s).tfstateterraform init -backend-config="bucket=central-terraform-state" ...
terraform state push ./backup-central-xxxx.tfstateSample Answer
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