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.

HardSystem Design

24 practiced

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.

MediumTechnical

20 practiced

Discuss the pros and cons of using managed cloud ML platforms (SageMaker, Vertex AI, Azure ML) for end-to-end ML vs integrating best-of-breed open-source tools (Kubeflow, Airflow, MLflow) on Kubernetes. From a cloud architecture perspective, which option better supports rapid experimentation vs production stability?

MediumTechnical

21 practiced

You need to choose a database for storing fast-changing metadata about training jobs, experiments, and model lineage. Compare using a relational database (e.g., Cloud SQL), a key-value store (e.g., DynamoDB), and a time-series database (e.g., InfluxDB) for this metadata. Which do you pick and why?

MediumTechnical

19 practiced

Write a Terraform snippet (HCL) to provision an auto-scaling group of GPU-enabled instances behind a load balancer suitable for model inference. Include variables for instance type, desired_capacity, min_size, and max_size. Explain how you would secure access to the instances and limit public exposure.

MediumSystem Design

22 practiced

Design a production-ready model serving architecture for a binary classification model that must handle 20k requests per second with 50ms P95 latency. Outline compute choices, caching, load balancing, autoscaling strategy, and how you'd manage model versions and canary rollouts. Assume cloud provider-managed services are available.

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