Start with your job, not a logo
Choose a credential that helps you perform target-role decisions. Business and foundational credentials emphasize concepts, use cases, responsible adoption, and service selection. Engineering credentials emphasize building, deploying, evaluating, operating, and securing systems.
Use official objectives and current status as the source of truth. PrepKloud marks legacy and transitioning paths separately, but providers can change names, versions, languages, and dates after publication.
Beginner and business routes
AWS Certified AI Practitioner suits learners who need AI, generative-AI, AWS service, and responsible-AI literacy. Microsoft AI Business Professional focuses on AI-assisted business work and responsible outcomes. Google Cloud Generative AI Leader is designed around business-level generative-AI strategy and Google Cloud capabilities.
Pick one foundation aligned to your environment. Then build a small evidence-based project instead of collecting all beginner badges.
Engineering routes
AWS Machine Learning Engineer Associate emphasizes production ML and current generative-AI/agentic-AI operations. Microsoft AI engineering paths focus on Azure AI applications, agents, and operationalization. Google Professional Machine Learning Engineer addresses designing and operating ML solutions. Databricks Generative AI Engineer emphasizes data-centric RAG and generative-AI workflows on the lakehouse.
Engineering learners should compare official objective weight, required platform experience, and hands-on services. Pair the selected credential with deployment, evaluation, security, monitoring, rollback, and cost evidence.
Handle exam transitions safely
Do not mix practice content from predecessor and successor versions without clear labels. Preserve useful legacy URLs, mark status and dates, link successors, and move active discovery to the current version. Rebuild your objective matrix when an exam guide changes.
Decision framework
| Area | Guidance |
|---|---|
| Path | Best fit |
| AWS AI Practitioner AIF-C01 | Foundational AWS AI and generative-AI literacy |
| Microsoft AI Business Professional AB-730 | Business users applying AI responsibly to knowledge work |
| Google Cloud Generative AI Leader | Business leaders evaluating Google Cloud generative AI |
| AWS Machine Learning Engineer Associate MLA-C02 | Engineers building and operating ML and generative-AI systems on AWS |
| Databricks Generative AI Engineer Associate | Engineers building data-grounded generative AI on Databricks |
Practical checklist
- Choose a target role and cloud
- Verify official status and objectives
- Compare objective depth, not only level labels
- Complete one aligned project
- Track weak domains with original practice
- Recheck the official page before scheduling
First-party sources
- AWS AI Practitioner
- Microsoft AI Business Professional
- Google Cloud Generative AI Leader
- Databricks Generative AI Engineer Associate
Source status last checked 2026-09-11. Links can change after publication.