DevOps Security and SRE

Apply the cloud shared-responsibility model to a security scenario in AWS and identify the corresponding Azure responsibilities.

Write an AWS IAM policy that grants least-privilege access to a specified resource.

Replace static AWS credentials with role-based temporary credentials for a workload.

Configure security groups and subnet placement for a tiered AWS application.

Select and configure an AWS KMS encryption approach for data at rest.

Trace an AWS resource change with CloudTrail and identify the equivalent evidence in Azure Activity Log.

Explain how declarative state and idempotency make infrastructure changes repeatable.

Author Terraform configuration that provisions parameterized AWS resources and exposes useful outputs.

Refactor repeated Terraform resources into a reusable module.

Configure remote Terraform state with locking while keeping secrets out of state and source control.

Use a Terraform plan to detect drift and assess the risk of a proposed change.

Add an infrastructure-as-code security check to a Git-based development workflow.

Trace how the Kubernetes control plane reconciles a submitted workload into running Pods.

Author Kubernetes Deployment and Service manifests for a containerized application.

Externalize application settings with ConfigMaps and handle sensitive values without embedding them in images.

Configure readiness probes, liveness probes, and resource requests and limits for a workload.

Grant a workload least-privilege Kubernetes permissions using a ServiceAccount and RBAC.

Diagnose a failed Kubernetes workload using status, events, logs, and container inspection.

Compare the operational responsibility boundaries of Amazon EKS and Azure AKS.

Design CI/CD stages and promotion criteria for a containerized service.

Implement a GitHub Actions workflow that tests an application and builds a versioned container image.

Use workload identity federation instead of long-lived cloud credentials in a deployment workflow.

Add static application and dependency security checks that fail a pipeline on an agreed policy threshold.

Generate a software bill of materials and scan a container image for known vulnerabilities.

Deploy a versioned application to Kubernetes from a controlled pipeline environment.

Configure branch protection, deployment approvals, and audit evidence for production changes.

Choose appropriate metrics, logs, or traces to investigate a specified service symptom.

Emit structured application logs with request correlation identifiers from a Python service.

Expose request rate, error, and duration metrics from an application.

Trace a request across service boundaries with OpenTelemetry context propagation.

Query application and cloud logs with CloudWatch Logs Insights and translate the query concept to Azure Monitor.

Build a dashboard that represents latency, traffic, errors, and saturation for a service.

Create an actionable alert based on fast and slow service-level-objective burn rates.

Identify single points of failure and critical dependencies in a service architecture.

Configure timeouts, bounded retries, exponential backoff, and jitter for a remote call.

Choose between circuit-breaker and bulkhead patterns for a specified dependency failure.

Run a load test and use telemetry to locate the first saturated resource.

Calculate required service capacity and safety headroom from workload measurements.

Configure Kubernetes horizontal scaling from an appropriate workload signal.

Translate a service threat model into prioritized preventive, detective, and corrective controls.

Store and retrieve application secrets with AWS Secrets Manager and compare the workflow with Azure Key Vault.

Prioritize a vulnerability using severity, exploitability, exposure, and business impact.

Restrict Kubernetes network paths with a default-deny NetworkPolicy and explicit allowances.

Enforce a container admission rule that rejects an unsafe workload configuration.

Investigate an AWS GuardDuty finding using CloudTrail and relevant workload evidence.

Define a rotation and recovery procedure for a compromised workload credential.

Assign incident severity and initial ownership from user impact and security indicators.

Use metrics, logs, traces, and system evidence to test a troubleshooting hypothesis.

Choose a low-risk mitigation that limits impact while preserving useful evidence.

Write a concise incident update for technical and non-technical stakeholders.

Create an executable runbook for a recurring operational alert.

Produce a blameless post-incident review with a timeline, contributing factors, and owned follow-up actions.

Set recovery time and recovery point objectives from a stated business requirement.

Design a backup and restore test that verifies both data integrity and recovery time.

Select an AWS Multi-AZ architecture for a stated availability requirement and identify its Azure counterpart.

Compare backup-and-restore, pilot-light, warm-standby, and active-active disaster-recovery strategies.

Run a scoped failure experiment with a steady-state check, abort condition, and rollback plan.

Evaluate a design change across reliability, security, operational complexity, and cloud cost.

Produce an architecture diagram and decision record for a provided cloud-native service.

Provision the project's AWS foundation from reviewed Terraform configuration.

Deploy the service to Kubernetes with least-privilege runtime permissions.

Implement a gated CI/CD path from source change to a versioned deployment.

Instrument the service and demonstrate its dashboard, service-level objective, and alerts.

Execute one load test and one controlled failure test, then record evidence-based findings.

Present a prioritized reliability and security improvement plan supported by project evidence.

Evaluate SRE and DevSecOps job descriptions to identify shared skills and role-specific expectations.

Map portfolio evidence to the competencies requested in a target job description.

Write evidence-based résumé bullets for reliability, automation, cloud, and security work.

Explain the portfolio system's architecture and trade-offs in a technical interview format.

Work through a timed production-troubleshooting scenario using hypothesis-driven reasoning.

Create a six-month development roadmap toward mid-level responsibility through deeper specialization, production exposure, and measurable ownership.