Amazon Web Services Architecture and Operations Questions

Advanced knowledge of Amazon Web Services platform services, architectural patterns, operational best practices, and trade offs. Candidates should be able to justify compute choices such as Amazon Elastic Compute Cloud instance types, instance sizing and performance tuning, and Auto Scaling strategies; storage and durability decisions including Amazon Simple Storage Service storage classes, versioning, lifecycle management, replication and archival strategies; database patterns such as Amazon Relational Database Service with multi availability zone deployments, read replicas and failover behavior, and Amazon DynamoDB capacity modes and throughput trade offs; networking design including Amazon Virtual Private Cloud topology, subnet and routing strategies, peering, gateway and interface endpoints, and network security controls; infrastructure as code and deployment patterns using Amazon CloudFormation including stack management and automated rollbacks; serverless and event driven design such as Amazon Web Services Lambda concurrency and cold start considerations and integration with Amazon API Gateway; content delivery and caching with Amazon CloudFront and Amazon ElastiCache including cache invalidation and expiry strategies; service specific operational concerns such as rate limiting, backup and restore, monitoring, logging, alerting and incident response; and cross cutting concerns including identity and access governance, cost optimization, disaster recovery planning and testing, and automation. Interview focus is on design reasoning, anticipating failure modes, scaling strategies, performance tuning, observability and automation, and provider specific operational practices.

HardBehavioral

79 practiced

Behavioral: Tell me about a time you had to argue for a significant infrastructure change (e.g., moving training to spot instances, standardizing on SageMaker, or adopting EFA). How did you present technical trade-offs, convince stakeholders, and measure success after the change?

MediumSystem Design

93 practiced

You are asked to integrate CI/CD for ML models: build and test container images, run model validation tests, push to ECR, and deploy to staging and production endpoints with approval gates. Sketch a pipeline using AWS CodePipeline/CodeBuild or GitHub Actions, and explain how to implement safe rollouts (canary/blue-green) and automated rollback based on metrics.

HardSystem Design

78 practiced

Architect an end-to-end multi-region active-active inference system that must serve global traffic with P95 latency <100ms and tolerate a full-region outage. Include model artifact distribution, real-time synchronization or eventual consistency of models, DNS routing (Route 53), data locality, and stateful user session handling.

HardSystem Design

76 practiced

Design end-to-end observability for an ML pipeline that includes data ingestion, transformations, model training, and serving. Specify what to emit as structured logs, metrics and traces, how to use CloudWatch, OpenTelemetry, and SageMaker Debugger, and how to correlate events across services for faster root-cause analysis.

HardSystem Design

83 practiced

Scenario: A regulatory auditor requests logs proving who accessed model artifacts and when, across accounts and regions. Design an audit trail solution using CloudTrail, S3 access logs, Athena queries, and central logging. Include retention, encryption, and how to produce tamper-evident evidence.

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