Choose AWS when your work uses Amazon cloud services, Bedrock, SageMaker, Glue or Redshift.
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Useful for understanding AWS AI capabilities and discussing AI solutions with technical teams. A starting point before implementation-focused exams.
A good first AI credential; learn basic AI concepts and AWS services.
Build a common cloud vocabulary before moving into a technical AWS role. It supports foundational understanding rather than proving production engineering ability.
Start here if you are new to cloud; pair it with basic AWS labs.
Supports roles that decide where AI creates value, secure investment and manage adoption. The exam assesses business judgment, not AWS implementation skills.
AWS recommends six months working with or alongside AI initiatives; coding is not required.
A direct operations credential for running AWS workloads, troubleshooting incidents and improving reliability. It is the current successor to the SysOps name.
AWS targets around one year deploying, managing and operating AWS workloads.
A direct fit for engineers building AWS data pipelines. Shows platform-specific knowledge that supports dependable analytics and AI data foundations.
Build SQL/Python pipelines and practise AWS data services before booking.
Useful for developers who build and maintain applications on AWS. Align preparation with a real deployed application rather than service memorisation.
Have practical programming experience and experience with AWS application services.
Fits engineers putting models into production and maintaining reliable ML systems on AWS. Adds operational depth beyond an AI foundation credential.
AWS targets about one year using SageMaker and other AWS ML engineering services.
Helps cloud engineers connect services into reliable designs. A useful architecture foundation before taking on larger AWS systems.
AWS recommends around one year designing cloud solutions.
Fits experienced engineers responsible for release automation and resilient AWS operations. Most useful alongside hands-on delivery ownership.
Build experience operating AWS systems and automating deployments before this professional exam.
A strong specialisation for developers building production generative AI applications on AWS, including secure retrieval, agent workflows and operational quality.
AWS targets 2+ years building production applications and one year implementing generative AI solutions.
A cloud-specific security specialisation for protecting AWS workloads and reviewing architectural security decisions.
Practise AWS security controls and incident investigation; intended for experienced practitioners.
Best for architects already making decisions across complex AWS environments. Supports architecture discussions that span several teams and applications.
AWS recommends 2+ years designing and implementing AWS solutions.
These are Global Certs IT editorial fit ratings, not official provider rankings or learner reviews. A 5/5 role match is directly aligned with the role; 4/5 adds supporting skills. Experience adjusts the score to help you distinguish a practical next step from a stretch goal.
With “Any experience”, ratings show role relevance alone. A credential supports your profile; practical skills, projects and your target employer’s technology still matter.
Official pages were checked on 2026-09-13. Bookable betas are labelled. Known retired exams are excluded from recommendations. Confirm exam versions, regional availability, eligibility and fees on the linked official page before paying.
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