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Find the right certifications for your career path. Each roadmap shows the recommended progression from entry-level to expert: with exam codes, experience expectations, and links to practice exams.
Cloud Engineers build and maintain cloud environments: provisioning resources, managing identities, configuring networking, and ensuring security and compliance.
Foundational understanding of cloud concepts, Azure services, pricing, SLAs, and compliance.
Entry-level understanding of AWS cloud, global infrastructure, pricing, and core services.
Manage Azure identities, storage, compute, virtual networking, and monitoring. The most common Azure cert for cloud engineers.
Design, build, test, and maintain cloud applications using Azure SDK, APIs, and DevOps integration.
Design secure, cost-optimized, and resilient architectures on AWS. Highly recognised industry-wide.
Design and implement CI/CD pipelines, infrastructure as code, monitoring, and security on Azure.
Design compute, network, storage, security, migration, and business continuity solutions on Azure.
Cloud Architects define the technical vision for cloud adoption: designing landing zones, governance models, migration strategies, and multi-cloud architectures that align with business goals.
Build foundational cloud knowledge before specialising in architecture.
AWS cloud fundamentals including Well-Architected Framework basics.
Hands-on Azure administration: essential practical knowledge for any architect.
Design resilient, cost-effective architectures on AWS. The gold-standard associate architect cert.
Design Azure infrastructure for compute, networking, storage, security, and business continuity at enterprise scale.
Design complex multi-account architectures, migration strategies, and hybrid connectivity on AWS.
Design enterprise-grade Google Cloud architectures. Enables multi-cloud expertise alongside Azure/AWS.
SRE practices, CI/CD, monitoring, and incident management on GCP.
DevOps Engineers automate everything: building CI/CD pipelines, managing container orchestration, implementing infrastructure as code, and ensuring system reliability through monitoring and SRE practices.
Cloud fundamentals as a prerequisite for Azure DevOps certs.
Required prerequisite for AZ-400. Builds the Azure admin foundation needed for DevOps.
Design and implement DevOps practices including CI/CD, IaC, monitoring, and security on Azure.
Industry gold standard for Kubernetes. Cluster setup, deployment, networking, storage, and troubleshooting. Vendor-neutral.
Advanced CI/CD, infrastructure automation, monitoring, and security on AWS requiring associate-level first.
Design, build, and deploy cloud-native applications on Kubernetes. Complements CKA for developer-focused DevOps roles.
Validate infrastructure as Code skills using Terraform: the industry-standard provisioning tool.
Automate provisioning, configuration management, and application deployment with Ansible.
Advanced Kubernetes security: cluster hardening, vulnerability management, and compliance.
SRE practices at Google scale: monitoring, incident management, and service reliability.
Security professionals safeguard cloud infrastructure: managing identity and access, securing networks, protecting data, responding to incidents, and ensuring regulatory compliance.
Foundational security, compliance, and identity concepts across Microsoft cloud services.
Globally recognised entry-level cybersecurity certification covering core security skills.
Manage identity, access, security operations, and data protection in Azure environments.
Design cybersecurity strategy across identity, security, compliance, and hybrid infrastructure.
Network Engineers design and manage the backbone of IT: routers, switches, firewalls, VPNs, SD-WAN, and cloud networking. The foundation of all IT infrastructure.
Industry-standard entry-level networking cert covering routing, switching, security, and automation.
Vendor-neutral networking fundamentals: cabling, topologies, protocols, and troubleshooting.
Advanced enterprise networking: routing, switching, SD-WAN, security, and automation at scale.
Design and implement Azure networking: virtual networks, hybrid connectivity, load balancing, and network security.
Design and implement complex AWS networking architectures including hybrid connectivity.
AI/ML Engineers bridge data science and software engineering: training and serving ML models, building data pipelines, deploying to production, and monitoring performance in cloud environments.
Foundational understanding of AWS cloud services, essential before specialising in AI/ML workloads on AWS.
Azure cloud fundamentals providing the baseline for Azure AI services and ML workflows.
Design, implement, and maintain ML solutions on AWS covering data engineering, modelling, and deployment.
Build, manage, and deploy AI solutions leveraging Azure Cognitive Services and Responsible AI practices.
Design, build, and operationalise data processing systems and ML pipelines on GCP at enterprise scale.
AWS Specialists go deep on a single cloud platform: progressing from fundamentals through associate-level architecture to professional-level design, DevOps automation, and security expertise.
Entry-level certification covering AWS cloud concepts, global infrastructure, pricing, and core services.
Design secure, cost-optimised, and resilient architectures on AWS. The most widely recognised AWS associate cert.
Design complex multi-account architectures, migration strategies, and hybrid connectivity at enterprise scale.
Advanced CI/CD, infrastructure automation, monitoring, and security automation on AWS.
Specialise in AWS security: incident response, infrastructure protection, identity management, and data protection.
Data Engineers architect and maintain the infrastructure that powers analytics and ML: building ETL/ELT pipelines, managing data warehouses and lakes, ensuring data quality, and enabling self-service analytics.
Cloud fundamentals covering core Azure data services and analytics concepts.
AWS cloud fundamentals including data storage and analytics service overview.
Design and implement data storage, processing, and security solutions using Azure data services.
Covers data engineering for ML: data ingestion, transformation, feature engineering, and MLOps pipelines on AWS.
Build and operationalise data processing systems end-to-end on GCP: from ingestion to analytics and ML.
Integrate AI capabilities into data pipelines: cognitive search, document intelligence, and knowledge mining.
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