Build the foundations
Practise your target language, data structures, Git and readable code.
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Present working software, tests and clear design choices.
Practise your target language, data structures, Git and readable code.
Build an API and database model with validation, authentication and error handling.
Add unit and integration tests, deployment automation and logs; investigate a performance or correctness issue.
Prepare a code walkthrough and design discussion. Explain ownership, trade-offs and what you would improve.
Your checklist stays in this page session only. It is a self-check, not a skills assessment.
A strong code portfolio and interview fundamentals usually come first. Add a platform certification only when it matches the role; an AI-cloud credential is relevant to AI application jobs, not every developer vacancy.
Useful for developers who build and maintain applications on AWS. Align preparation with a real deployed application rather than service memorisation.
Before booking: Have practical programming experience and experience with AWS application services.
Fits backend developers building the cloud services that support AI applications through the full development lifecycle.
Before booking: Build backend development, Azure deployment and application-security experience.
Review the official source for fees, assessment format, prerequisites and award/renewal requirements. Guidance reviewed 13 September 2026.
Build an authenticated booking API with concurrency handling, tests, a deployment pipeline and an operating guide.
Use these original practice scenarios to structure your answers. They are not leaked exam questions or an employer’s interview script.
A strong answer covers: Discuss database constraints, transactions, race conditions and a concurrency test.
A strong answer covers: Use controlled test doubles or an isolated service and verify timeout, retry and user-visible behaviour.