Every exam here is tracked against the provider’s own published guide, with the version code, the domain weightings and the date we last checked them. Where an outline has not been imported yet, the page says so rather than guessing.
The entry point to the AWS track.
A foundational certification covering artificial intelligence, machine learning and generative AI concepts, and the AWS services that implement them.
The most widely held cloud certification there is.
Writing, deploying and debugging applications on AWS: SDKs, IAM from an application’s point of view, Lambda, API Gateway, DynamoDB access patterns, and the deployment and observability tooling around them..
Operating AWS workloads day to day: monitoring, automation, cost and performance work, networking, security controls and business continuity.
Building and operating data pipelines on AWS — ingestion, transformation, storage design, orchestration, and the governance and security that has to travel with the data..
Multi-account, multi-region architecture at organisational scale: migration strategy, cost governance across business units, network design, and continuous improvement of existing estates.
Threat detection and incident response, infrastructure and data protection, identity and access management at depth, and the logging and monitoring that makes any of it provable..
Cloud concepts in general and Azure in particular: the shared responsibility model, service types, the region and resource hierarchy, core compute, networking and storage services, and the governance and cost tooling around them..
Core data concepts and how they map onto Azure: relational and non-relational stores, analytics workloads, and the services Microsoft provides for each..
The day job of an Azure administrator: identity and governance, storage, compute, virtual networking, and monitoring and recovery.
Developing solutions for Azure: compute options for application code, storage from an application’s point of view, authentication and authorisation, and the monitoring, caching and integration services around them..
Building AI solutions on Azure — vision, language, speech, document intelligence, search and generative AI — and the planning, security and monitoring that production use of them requires..
Designing identity, governance, monitoring, data storage, business continuity and infrastructure solutions on Azure.
Cloud concepts and Google Cloud products framed around business outcomes: digital transformation, infrastructure and application modernisation, data and AI, and the operational side of running in the cloud..
Deploying, securing and operating workloads on Google Cloud: project and billing setup, compute and storage choices, networking, monitoring and logging, and IAM.
Designing and planning cloud solution architecture, managing infrastructure, designing for security and compliance, and optimising business and technical processes.
Configuring access, network security, data protection and operations on Google Cloud, and ensuring compliance.