Amazon DevOps Engineer Interview Questions (2026)

1. 1Online Assessment (SDE OA)60 minMedium

   Two timed coding problems plus a workplace-simulation and logic section; the main gate for freshers and India volume hiring.

2. 2Phone screen45 minMedium

   One coding problem plus 1-2 Leadership Principle STAR questions with an SDE.

3. 3Coding loop round60 minMedium

   DSA problem to working code, followed by assigned-LP behavioral questions in STAR format.

4. 4System design loop round60 minHard

   Design an Amazon-scale service with capacity math, plus LPs; low-level/OOD design substitutes for junior candidates.

5. 5Hiring Manager round45 minMedium

   Team fit, project deep dives, and Deliver Results/Bias for Action stories with the manager you would report to.

6. 6Bar Raiser60 minHard

   An interviewer from outside the team stress-tests LP stories and overall bar with the hardest cross-examination of the loop; holds veto.

1. Q1

   A production service (high-volume customer-facing retail/service platform) is failing after a change involving VPC subnet design and routing. Walk through how you would investigate, mitigate, and fix it. Assume the target company is Amazon and the priority is multi-region recovery with a documented RTO/RPO

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer starts with impact, recent changes, and evidence before changing production. For VPC subnet design and routing: Separate public and private subnets across Availability Zones, keep route tables explicit, use NAT only where needed, and prove connectivity with flow logs and route analysis. Design for blast-radius containment. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Check logs, metrics, events, deployment diffs, permissions, dependencies, and rollback options; then write a durable fix and postmortem item.

2. Q2

   Design a production AWS approach using Auto Scaling and load balancing. The service context is high-volume customer-facing retail/service platform, and it must handle low-latency global user experience. How do you structure the solution and tradeoffs? Assume the target company is Amazon and the priority is low-latency global user experience. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

   HardAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer turns requirements into architecture, controls, automation, and measurable failure handling. For Auto Scaling and load balancing: Use target-tracking or scheduled scaling behind an ALB/NLB, health checks that reflect real readiness, and conservative cooldowns. Measure request latency, queue depth, saturation, and error rates before tuning. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Include IaC, CI/CD, monitoring, security boundaries, capacity assumptions, and the exact rollback or failover path.

3. Q3

   During on-call, user impact points toward Route 53 DNS and health checks. What do you do in the first 15 minutes, the next hour, and after recovery? Assume the target company is Amazon and the priority is 99.9% availability with fast rollback

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer prioritizes mitigation, communication, evidence, and prevention. For Route 53 DNS and health checks: Use Route 53 for hosted zones, weighted/latency/failover routing when justified, short TTLs during migrations, and health checks that reflect user-visible availability rather than only instance reachability. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Declare severity, assign roles, mitigate first, communicate cadence, preserve timeline, and convert root cause into tested corrective actions.

4. Q4

   Compare two viable approaches to Multi-account landing zones for this context: high-volume customer-facing retail/service platform. What would make you choose one over the other? Assume the target company is Amazon and the priority is tenant isolation for enterprise customers. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer compares constraints instead of declaring a universal best practice. For Multi-account landing zones: Separate workloads by account for isolation, centralize logging/security tooling, use SCPs and IAM Identity Center, and make account vending reproducible through IaC. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. State the decision criteria: risk, team expertise, operational load, lock-in, cost, compliance, and reversibility.

5. Q5

   An interviewer asks for a deep dive on Disaster recovery and regional resilience for this context: high-volume customer-facing retail/service platform. What implementation details, failure modes, and observability would you cover? Assume the target company is Amazon and the priority is multi-region recovery with a documented RTO/RPO. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer goes beyond commands into internals, failure modes, and observability. For Disaster recovery and regional resilience: Define RTO/RPO, select backup/restore, pilot light, warm standby, or active-active accordingly, and test failover. Multi-region is valuable only if data, DNS, deployment, and operations are ready. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Cover control plane/data plane behavior, state, dependencies, permissions, edge cases, and how you would observe it during failure.

6. Q6

   During on-call, user impact points toward Auto Scaling and load balancing. What do you do in the first 15 minutes, the next hour, and after recovery? Assume the target company is Amazon and the priority is strict auditability and least-privilege access

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer prioritizes mitigation, communication, evidence, and prevention. For Auto Scaling and load balancing: Use target-tracking or scheduled scaling behind an ALB/NLB, health checks that reflect real readiness, and conservative cooldowns. Measure request latency, queue depth, saturation, and error rates before tuning. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Declare severity, assign roles, mitigate first, communicate cadence, preserve timeline, and convert root cause into tested corrective actions.

7. Q7

   You need to migrate legacy production usage of EKS/ECS/Lambda tradeoffs in this context: high-volume customer-facing retail/service platform, without downtime. How would you plan and execute it? Assume the target company is Amazon and the priority is 99.9% availability with fast rollback. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

   MediumAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer uses inventory, compatibility, staged rollout, verification, and rollback. For EKS/ECS/Lambda tradeoffs: Choose Lambda for event-driven short tasks, ECS for simpler managed containers, and EKS when Kubernetes portability/ecosystem control is worth the operational overhead. Compare scaling behavior, team skills, and compliance needs. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Run dual-write or shadow traffic where appropriate, compare outputs, migrate cohorts, monitor error budgets, and keep a rollback window.

8. Q8

   Tell me about a time you used AWS or Route 53 DNS and health checks to improve customer obsession, ownership, frugality, and operational excellence. What did you measure and learn? Assume the target company is Amazon and the priority is tenant isolation for enterprise customers. Use a Amazon-style example and include measurable production impact

   MediumLeadership Principles / Bar RaiserAWS

   How to answer:

   A strong answer uses STAR: situation, task, action, result, and lesson learned. For Route 53 DNS and health checks: Use Route 53 for hosted zones, weighted/latency/failover routing when justified, short TTLs during migrations, and health checks that reflect user-visible availability rather than only instance reachability. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Quantify impact with latency, availability, cost, deployment frequency, MTTR, defect rate, or toil reduction.

9. Q9

   A production service (high-volume customer-facing retail/service platform) is failing after a change involving KMS and secrets integration. Walk through how you would investigate, mitigate, and fix it. Assume the target company is Amazon and the priority is rapid incident detection and mitigation

   EasyAWS + Terraform Deep DiveAWS

   How to answer:

   A strong answer starts with impact, recent changes, and evidence before changing production. For KMS and secrets integration: Encrypt sensitive data with KMS-managed keys, rotate or reissue secrets through Secrets Manager or Parameter Store, restrict decrypt permissions, and avoid placing secret values in logs, AMIs, or Terraform state. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Check logs, metrics, events, deployment diffs, permissions, dependencies, and rollback options; then write a durable fix and postmortem item.

10. Q10

    Workload in this production context (high-volume customer-facing retail/service platform) grows 10x. How would you scale and protect Disaster recovery and regional resilience? Assume the target company is Amazon and the priority is secure delivery of regulated workloads

    MediumAWS + Terraform Deep DiveAWS

    How to answer:

    A strong answer measures the bottleneck before adding capacity and protects downstream dependencies. For Disaster recovery and regional resilience: Define RTO/RPO, select backup/restore, pilot light, warm standby, or active-active accordingly, and test failover. Multi-region is valuable only if data, DNS, deployment, and operations are ready. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Use load tests, autoscaling policies, queue/backpressure controls, quota reviews, and cost alarms; confirm the user-facing SLI improves.

11. Q11

    Explain how to apply pipeline stages and gates in CI/CD for this production context: high-volume customer-facing retail/service platform. What problem does it solve, and where can it fail? Assume the target company is Amazon and the priority is strict auditability and least-privilege access

    EasyTechnical ScreenCI/CD

    How to answer:

    A strong answer defines the mechanism, names the operational boundary, and states when it is the right tool. For pipeline stages and gates: Structure pipelines into build, test, scan, package, deploy, and verify stages. Gate promotion using automated evidence, not manual habit. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Mention how you would validate the behavior in a non-production environment and what metric proves it is working.

12. Q12

    Compare two viable approaches to IaC in delivery pipelines for this context: high-volume customer-facing retail/service platform. What would make you choose one over the other? Assume the target company is Amazon and the priority is secure delivery of regulated workloads. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

    MediumCI/CD and Deployment AutomationCI/CD

    How to answer:

    A strong answer compares constraints instead of declaring a universal best practice. For IaC in delivery pipelines: Run format/validate/plan, require plan review, apply with locked state, and separate credentials per environment. Emit audit trails for each change. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. State the decision criteria: risk, team expertise, operational load, lock-in, cost, compliance, and reversibility.

13. Q13

    An interviewer asks for a deep dive on pipeline stages and gates for this context: high-volume customer-facing retail/service platform. What implementation details, failure modes, and observability would you cover? Assume the target company is Amazon and the priority is minimal operational toil for a small platform team. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

    HardCI/CD and Deployment AutomationCI/CD

    How to answer:

    A strong answer goes beyond commands into internals, failure modes, and observability. For pipeline stages and gates: Structure pipelines into build, test, scan, package, deploy, and verify stages. Gate promotion using automated evidence, not manual habit. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Cover control plane/data plane behavior, state, dependencies, permissions, edge cases, and how you would observe it during failure.

14. Q14

    Workload in this production context (high-volume customer-facing retail/service platform) grows 10x. How would you scale and protect artifact versioning? Assume the target company is Amazon and the priority is 99.9% availability with fast rollback

    MediumCI/CD and Deployment AutomationCI/CD

    How to answer:

    A strong answer measures the bottleneck before adding capacity and protects downstream dependencies. For artifact versioning: Build once and promote the same immutable artifact through environments. Attach metadata such as commit SHA, SBOM, test results, and provenance. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. Use load tests, autoscaling policies, queue/backpressure controls, quota reviews, and cost alarms; confirm the user-facing SLI improves.

15. Q15

    Compare two viable approaches to branching and release strategy for this context: high-volume customer-facing retail/service platform. What would make you choose one over the other? Assume the target company is Amazon and the priority is high deployment velocity without increasing incidents. Frame the answer for an interview loop where expect data-backed tradeoffs, failure analysis, and STAR examples tied to Leadership Principles

    MediumCI/CD and Deployment AutomationCI/CD

    How to answer:

    A strong answer compares constraints instead of declaring a universal best practice. For branching and release strategy: Keep branches short-lived when possible, use protected branches, and define release channels clearly. Match strategy to deployment frequency and regulatory requirements. In a high-volume customer-facing retail/service platform, tie the decision to customer obsession, ownership, frugality, and operational excellence. State the decision criteria: risk, team expertise, operational load, lock-in, cost, compliance, and reversibility.

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Prepare 8-12 STAR stories mapped to Leadership Principles; expect a Bar Raiser; quantify impact

Indicative DevOps Engineer pay in India: ~₹10–45 LPA (role-level range, not a Amazon-specific figure).

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