Cloud Architecture Fundamentals Questions

Fundamental concepts and design patterns for cloud based systems and services. Topics include core service categories such as compute, storage, networking and databases, virtual machines and containers, serverless computing, managed services, and infrastructure as code. Understand deployment and service models including infrastructure as a service, platform as a service, and software as a service. Evaluate architectural patterns including monolithic, microservices, and serverless approaches, and how they influence scalability, availability, reliability, performance, security, and cost. For more senior roles include distributed systems concepts, consistency and partitioning models, trade off analysis, fault isolation, observability and operational practices in cloud native design.

MediumSystem Design

20 practiced

Create a high-level network design for a secure VPC hosting ML infrastructure that includes: private subnets for training, public subnets for inference gateways, a bastion host for admin access, and access to managed storage. Indicate how you would use security groups, NACLs, and VPC endpoints to limit exposure and minimize data egress.

MediumTechnical

27 practiced

Explain how network egress costs can unexpectedly increase in ML workloads and list five mitigation strategies at the architecture and operational levels to reduce egress cost without harming model quality or availability.

HardSystem Design

25 practiced

Design an observability plan for ML systems in production. Specify which metrics, logs, traces, and business KPIs you would collect for: model serving, feature pipelines, and training jobs. Explain how you'd detect data drift, model performance degradation, and infrastructure resource saturation.

MediumTechnical

20 practiced

Explain the concept of model explainability and how cloud architecture choices affect your ability to provide explanations at inference time. For example, discuss latency constraints, model introspection, and use of pre-computed explanation caches.

MediumTechnical

20 practiced

Explain how to set SLOs, SLIs, and error budgets for an ML inference service. Provide examples of SLIs for availability, latency, and model quality, and describe how the error budget would influence operational decisions like feature rollout or throttling.

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