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

0 practiced

Your distributed training jobs suffer from frequent IO stalls when reading large datasets from S3. Describe how you would investigate and remediate: parallelism at dataset read layer, S3 request rates and hot prefixes, using S3 Transfer Acceleration, leveraging Amazon FSx for Lustre or EFS, and tuning instance network/IO settings.

MediumTechnical

0 practiced

As part of your ML platform, propose an incident response playbook for a degraded model endpoint: symptoms include increased 95th percentile latency and 5xx errors. Outline steps to diagnose, rollback, scale, and notify stakeholders. Mention CloudWatch dashboards, runbooks, and automated rollback triggers.

EasyTechnical

0 practiced

Describe the differences between Amazon RDS Multi-AZ deployments and Read Replicas. For an ML metadata service that stores model metadata, experiment runs and small feature tables, explain when you would use Multi-AZ, synchronous vs asynchronous replication, and read replicas. Include failover behavior and consistency trade-offs.

HardTechnical

0 practiced

You need to move from a monolithic ML model artifact to a quantized and pruned version to reduce inference cost. Explain how to integrate container image optimization, model quantization, and SageMaker Neo or TensorRT builds into your deployment pipeline, and how to validate functional parity and performance gains.

MediumTechnical

0 practiced

You have a model-serving API that occasionally receives traffic spikes and some malicious clients performing high-frequency requests. Propose a layered rate-limiting and protection strategy using API Gateway, WAF, CloudFront, and token bucket semantics. Explain per-client vs global throttling and how to avoid impacting legitimate bursty clients.

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