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.

MediumTechnical

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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.

MediumTechnical

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Explain eventual consistency vs strong consistency and give two concrete examples where eventual consistency is acceptable in ML pipelines and two where strong consistency is required. How do consistency choices affect system design for feature stores and model metadata?

EasyTechnical

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Describe the purpose of a container orchestration system like Kubernetes for machine learning workloads. List three Kubernetes features that are especially useful for ML engineers and explain how they help with training or inference workloads.

HardSystem Design

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Design a CI/CD pipeline for ML models that covers: reproducible training, testing (unit, integration, data validation), model validation, packaging, deployment to a staging environment, and automated canary rollouts to production. Mention cloud services or tools you would use for each stage and how you'd gate promotions between stages.

HardSystem Design

0 practiced

Design an architecture for a feature pipeline that guarantees exactly-once processing semantics for critical feature updates derived from streaming events. Explain which streaming frameworks and storage patterns you'd use and how you'd prevent duplicate feature writes on retries.

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