Cloud Computing Test

Evaluates core cloud components across leading providers (AWS, Azure, GCP), including provisioning compute resources (VMs, auto-scaling groups), selecting appropriate storage types (object, block, file), setting lifecycle policies, and configuring VPCs, routing tables, and VPNs. Also includes high availability configurations like load balancers, and bandwidth planning for hybrid environments.

Tests understanding of secure identity and access management (IAM), policies, roles, trust relationships, and resource-level permissions. Includes encryption techniques, secure key and secret storage (KMS, Vault), Zero Trust Architecture, multi-factor authentication (MFA), and service-to-service authentication. Advanced focus on secure API exposure, token-based auth (JWT, OAuth2), and compliance scenarios (GDPR, HIPAA).

Covers codification of infrastructure using tools like Terraform, AWS CDK, Azure Bicep, and GCP Deployment Manager. Includes state management, modular architecture, provisioning reusable environments, and CI/CD automation via pipelines (GitHub Actions, CodePipeline, Azure DevOps). Assesses skills in version control, configuration drift detection, and role-based IaC practices in production.

Focuses on end-to-end use of cloud-native AI/ML platforms like AWS SageMaker, Azure ML, and Google Vertex AI for model development, training, tuning, and deployment. Topics include notebooks, model registries, AutoML, endpoint deployment, real-time and batch inference, model explainability (e.g., SHAP, LIME), and integration with data sources. Reflects typical responsibilities in AI engineering or MLOps roles.

Assesses ability to design and manage scalable, secure data pipelines in cloud environments using services like AWS Glue, Azure Data Factory, and Google Cloud Dataflow. Topics include schema evolution, transformations, orchestration, partitioning, error handling, and integration with data lakes and warehouses (e.g., BigQuery, Snowflake). Also covers batch vs streaming data flows, metadata management, and pipeline cost optimization.

Evaluates knowledge of packaging, deploying, and managing containerized applications using Docker and orchestrators like Kubernetes (GKE, EKS, AKS). Includes topics like pod scheduling, persistent storage, service mesh (Istio, Linkerd), autoscaling (HPA/VPA), Helm charts, and secrets management. Critical for deploying AI/ML workloads in microservices or multi-tenant cloud environments.

Covers monitoring strategies using native tools like CloudWatch, Azure Monitor, and GCP Operations Suite. Includes log management, metric collection, trace analysis, and alert configuration. Also assesses knowledge of billing metrics, budget alarms, resource tagging, and use of tools like AWS Cost Explorer and Azure Cost Management to optimize usage and control costs. Emphasizes operational excellence and reliability.

Examines CI/CD principles applied to AI/ML, including model versioning, experiment tracking, A/B testing, rollback, canary deployment, drift detection, and continuous retraining. Covers tools like MLflow, DVC, SageMaker Pipelines, and Kubeflow. Also includes general DevOps concepts like infrastructure as code pipelines, GitOps, and blue/green deployments in AI pipelines.

Tests ability to architect enterprise-grade cloud solutions for AI workloads, integrating HA/DR principles, compliance boundaries, hybrid models, and global deployments. Includes serverless integration, edge computing (e.g., Greengrass, IoT Edge), cost-effective scaling, and vendor selection strategies. Strategic topics include governance, FinOps, platform standardization, and aligning cloud efforts with business KPIs.