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.

HardTechnical

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Design a secure model hosting architecture for a proprietary model that must not leave the customer's account and must be accessible only to authenticated private clients. Include KMS key management, private endpoints, mutual TLS or sigv4, S3 bucket policies, ECR private images, and auditing. Explain how you prevent model exfiltration.

HardTechnical

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Technical-coding: Provide a small AWS CLI or SDK pseudo-script (bash or Python) that automates taking a snapshot of an RDS instance, exports it to S3, and starts an automated test restore into a non-production account/region. Explain how IAM roles and cross-account permissions should be configured.

EasyTechnical

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Explain Auto Scaling groups (ASG) basic scaling policies. For an inference fleet behind an Application Load Balancer, what metrics would you use (CPU, request latency, custom application metrics) and what are risks of scaling only on CPU?

MediumTechnical

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You need to run sensitive training jobs in a VPC without NAT gateway egress to minimize cost and surface area. Explain how you would design VPC endpoints (gateway and interface), S3 access, and access to AWS ECR or Sagemaker so training jobs can download data and push logs securely. Discuss trade-offs and limitations.

MediumTechnical

0 practiced

You must deploy an ensemble of models using SageMaker multi-model endpoints vs separate endpoints per model. Compare these approaches in deployment complexity, memory utilization, cold-start behavior, and update/rollback strategies for models updated daily.

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