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.

MediumSystem Design

69 practiced

Design a VPC and routing plan to support a multi-account machine learning platform where data ingestion account writes large datasets to a central S3 account, and training accounts must pull data without crossing the public internet. Explain use of IAM roles, bucket policies, VPC endpoints, and cross-account replication if needed.

HardTechnical

75 practiced

Design a cost-optimization plan for an AI platform with monthly spend of $180k: 60% training, 30% inference, 10% storage/ops. Provide specific levers: spot/Reserved/Savings Plans, instance right-sizing, multi-tier storage strategy, model quantization/packing to reduce inference resource use, and automation to shut down idle resources.

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.

EasyTechnical

75 practiced

As an AI Engineer, explain the different Amazon EC2 instance families (general purpose, compute-optimized, memory-optimized, storage-optimized, GPU-accelerated) and recommend specific instance types for two tasks: (A) large-scale CPU-bound data preprocessing pipelines, and (B) distributed GPU training for transformer models. Justify your choices with respect to vCPU, memory, network bandwidth, GPU model, NVMe/instance-store vs EBS, and cost trade-offs.

MediumTechnical

84 practiced

Describe a backup and restore strategy for: (A) model artifacts and registry metadata, (B) training datasets stored in S3, and (C) a DynamoDB-based feature store. Include point-in-time recovery, cross-region replication, lifecycle, and estimated RTO/RPO trade-offs.

Unlock Full Question Bank

Get access to hundreds of Amazon Web Services Architecture and Operations interview questions and detailed answers.

Join thousands of developers preparing for their dream job.