CI/CD or AWS infrastructure broken.Fixed this session.

A GitHub Actions pipeline that fails mid-deploy and leaves production in a broken state requires both an immediate rollback and a pipeline fix. For the immediate rollback, the engineer uses the previous successful workflow run artifact or a tagged Docker image to redeploy the last known-good state. For the root cause, the engineer inspects the workflow YAML step that failed: commonly a missing IAM permission on the OIDC role assumed by the workflow, a secret not set in the repository environment (distinct from the repository secrets), a Docker push command failing because the ECR login step uses a deprecated action version, or a deployment step not using continue-on-error or a proper health check gate. A blue-green or canary deployment strategy is recommended as a follow-up so future failures only affect the new version and rollback is automatic.

ECS task health check failures are diagnosed by: checking the ECS service events in the AWS console for the exact reason the task was deregistered, inspecting CloudWatch Logs for the container startup output to see if the application crashed before the health check endpoint became available, verifying the task definition health check command and grace period (startPeriod should be set to at least the application startup time), checking the security group on the ECS task to ensure the ALB security group is allowed to reach the container port on the health check endpoint, and confirming the application is listening on the container port defined in the task definition (not on localhost). The most common fix is increasing the healthCheckGracePeriodSeconds to give slow-starting JVM or Python services time to initialize.

RDS connection pool exhaustion is diagnosed using the RDS DatabaseConnections CloudWatch metric compared to the max_connections parameter for your instance class (visible in RDS Parameter Groups). The application-side fix is almost always: the connection pool is sized too large relative to max_connections, the connection pool is not being released correctly causing connections to accumulate (particularly in Lambda functions that open a new pool on each invocation), or a long-running query is holding a connection for minutes and starving other requests. The immediate fix for Lambda workloads is adding RDS Proxy between Lambda and RDS, which multiplexes thousands of Lambda connections down to a pool of database connections. For application server workloads, the fix is right-sizing the pool max-size to max_connections / number_of_instances with headroom for admin access.

S3 static website 403 errors are traced by checking: whether Block Public Access is enabled on the bucket (which overrides ACLs and bucket policies for public reads), whether the bucket policy grants s3:GetObject to Principal: * with the correct resource ARN pattern (arn:aws:s3:::bucket-name/*), whether the CloudFront distribution in front of the bucket is using Origin Access Control (OAC) and the bucket policy grants access to the CloudFront service principal, or whether the bucket is serving files via a static website endpoint vs the REST API endpoint (these require different URL formats). The engineer checks the S3 server access logs or CloudTrail for the exact 403 response, identifies whether the error is on the bucket policy or the ACL evaluation path, and applies the minimal policy change to restore access.

Every QuickHire DevOps AWS engineer passes: a live incident simulation involving a failing GitHub Actions ECS deployment with a hidden IAM OIDC trust policy error and an RDS connection pool exhaustion, a written assessment covering AWS services in depth (ECS task networking, RDS Proxy, S3 bucket policy evaluation order, CloudFront OAC vs OAI, IAM policy conditions), CI/CD pipeline patterns (GitHub Actions environments and approvals, ArgoCD GitOps, CodeDeploy blue-green), Terraform internals (state locking, module versioning, import blocks, moved blocks), and observability (CloudWatch Logs Insights, Container Insights, X-Ray tracing), plus a portfolio review of production AWS infrastructure managed as code. Only the top 3% pass all stages.

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