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AWS has expanded its AI certification pathway, but it has not launched several entirely new exams. The main new credential is the AWS Certified Generative AI Developer – Professional. AWS also offers the existing AI Practitioner certification, is replacing Machine Learning Engineer – Associate MLA-C01 with MLA-C02, and retired the Machine Learning – Specialty exam after March 31, 2026.
For your budget, the key question is not which credential sounds most advanced. It is whether the exam matches your job target closely enough to justify the fee, preparation time, training costs, and possible AWS usage charges.
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What AWS actually changed
As of September 14, 2026, AWS’s AI certification portfolio is best understood as a layered pathway:
- AI Practitioner: foundational AI and machine-learning literacy.
- Machine Learning Engineer – Associate: production ML implementation and operations, with MLA-C02 replacing MLA-C01.
- Generative AI Developer – Professional: advanced development of production generative-AI applications.
- Microcredentials and labs: practical learning and assessment that complement certifications but do not replace them.
The older Machine Learning – Specialty exam is retired for new candidates. Existing holders retain it until their normal expiration date. AWS’s current exam-guide index lists AI Practitioner, Machine Learning Engineer – Associate, and Generative AI Developer – Professional in the active portfolio.
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See AWS’s portfolio announcement and official exam-guide index.
Certification costs and formats
The following are official U.S.-dollar list prices shown in AWS materials. Taxes, currency conversion, regional checkout rules, and test-center availability can change the final cost.
| Credential | Level | Exam price | Time | Questions | Status |
|---|---|---|---|---|---|
| AWS Certified AI Practitioner, AIF-C01 | Foundational | $100 | 90 minutes | 65 | Available |
| AWS Certified Machine Learning Engineer – Associate, MLA-C01 | Associate | $150 | 130 minutes | 65 | English version ends September 28, 2026 |
| MLA-C02 beta | Associate | $75 beta price | 170 minutes | 85 | Beta delivery begins September 29, 2026 |
| AWS Certified Generative AI Developer – Professional, AIP-C01 | Professional | $300 | 180 minutes | 75 | Standard exam available |
Exam delivery is through Pearson VUE at a test center or by online proctoring, although language and regional availability vary. The Generative AI Developer – Professional exam is offered in English, Japanese, Korean, and Simplified Chinese. MLA-C02’s beta is English-only.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAWS Certified Generative AI Developer – Professional
This is the major genuinely new certification. It is aimed at experienced software developers and AI engineers who build applications, not simply people who use chatbots or understand AI terminology.
AWS’s intended candidate generally has at least two years of experience building production-grade applications, general AI/ML or data-engineering experience, and at least one year of hands-on generative-AI implementation. The exam also assumes familiarity with compute, storage, networking, IAM and security, deployment, infrastructure as code, monitoring, observability, and cost optimization.
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Topics include foundation-model integration, prompt and application design, retrieval-augmented generation, vector databases, Amazon Bedrock, agents, security, reliability, cost efficiency, evaluation, and responsible AI. The refreshed standard exam also includes Bedrock AgentCore.
Financially, this is a poor first purchase for most beginners. At $300 before taxes and preparation expenses, it makes more sense when an employer, client, or target role specifically values production generative-AI development on AWS. It is not a substitute for hands-on experience, and passing it does not prove that someone can independently operate a reliable, secure, cost-controlled AI system.
AWS Certified AI Practitioner
AI Practitioner costs $100 and is designed for people who understand AI and ML concepts but do not necessarily build AI/ML solutions. Suitable candidates include business analysts, product and project managers, IT support workers, sales and marketing professionals, managers, and technical workers seeking a foundation before deeper study.
The exam covers AI and ML concepts, generative-AI use cases, responsible AI, and AWS AI services at a foundational level.
It can be a sensible low-cost credential when the goal is AI literacy or when a candidate needs AWS context before pursuing an associate or professional exam. However, it should not be presented as proof of ability to write application code, deploy Bedrock workloads, build RAG systems, operate SageMaker pipelines, or troubleshoot AI latency, reliability, security, and cost.
Rank #3
People entirely new to cloud may benefit from AWS Cloud Practitioner Essentials or AWS Technical Essentials first. AWS’s AI Practitioner page provides the official preparation route and exam details.
MLA-C01 versus MLA-C02
Machine Learning Engineer – Associate is aimed at ML engineers, MLOps engineers, data engineers, DevOps professionals supporting ML workloads, backend developers moving into ML systems, and data scientists who operationalize models.
The current MLA-C01 focuses on implementing and operationalizing production ML workloads, including SageMaker and other AWS ML services. AWS lists at least one year of relevant experience as the intended background.
MLA-C02 expands the emphasis to foundation models, large language models, generative-AI implementation, agentic-AI workflows, Amazon Bedrock, responsible AI, and production-scale operations.
Should you take MLA-C01 now?
- Take MLA-C01 before September 28, 2026 if you are already prepared, need the credential immediately, or require the current certification for an employer or application.
- Consider MLA-C02 beta if you want newer generative-AI and agentic-AI coverage, can take an English-only exam, and do not need the standard multilingual version immediately.
MLA-C02 beta registration opened September 1, 2026. Beta delivery begins September 29, 2026, costs $75, contains 85 questions, lasts 170 minutes, and is expected to be available in its standard form in early 2027. AWS has not published the complete detailed MLA-C02 task statements in the supplied information, so candidates should not rely on unofficial claims about exact domain percentages.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11AWS says an existing certification remains active through its original expiration date. Review the official MLA-C02 announcement before booking.
Which AWS AI certification fits your situation?
| Your goal | Best starting point | Why |
|---|---|---|
| Understand AI for management, product, sales, or analysis | AI Practitioner | Lowest-cost formal AI credential and foundational scope |
| Build Bedrock, RAG, or agent applications | Generative AI Developer – Professional | Closest match for advanced production application development |
| Operate ML pipelines, deployment, and monitoring | MLA-C01 or MLA-C02 | Better aligned with ML engineering and MLOps |
| Build data pipelines supporting AI | Data Engineer – Associate, then MLA-C02 or AI Practitioner | Data quality and pipelines are prerequisites for production AI |
| New to AWS and cloud | Cloud Practitioner first, then AI Practitioner | Provides general AWS context before specialization |
| Secure AI workloads | Security – Specialty plus AI/ML security study | Security is a different role from AI application development |
The Professional exam has no required prerequisite certification. AWS says candidates may nevertheless benefit from AI Practitioner, Solutions Architect – Associate, Machine Learning Engineer – Associate, or Data Engineer – Associate beforehand.
Certification, course, badge, and microcredential are not the same
- Certification: a formal AWS exam credential.
- Certificate of completion: evidence that someone completed a course, not an equivalent to certification.
- Microcredential: a narrower practical assessment.
- Digital badge: a shareable representation of a credential or learning achievement.
- Skill Builder course or lab: training and practice, not itself an AWS certification.
AWS positions credentials such as Agentic AI Demonstrated and MLOps Demonstrated as practical assessments in provisioned AWS environments that complement certifications. They can strengthen a portfolio, but they do not turn a foundational exam into an engineering qualification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The real cost is more than the exam fee
Budget for the complete preparation plan rather than only the booking price:
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →- Training: Skill Builder subscriptions, classroom courses, or third-party materials.
- Practice: official practice questions, practice exams, and pretests.
- Hands-on work: AWS usage charges for compute, storage, model calls, databases, and networking.
- Time: study hours and time spent building a project.
- Retakes: another exam fee if you do not pass.
- Travel or equipment: possible costs for a test center or a suitable online-proctored setup.
- Taxes and currency conversion: the listed U.S.-dollar price is not necessarily the final checkout total.
AWS Skill Builder offers exam-preparation plans, official questions, Builder Labs, SimuLearn, Cloud Quest, AWS Jam, and microcredentials. Free accounts provide limited access to many learning experiences; broader access may require a subscription. Check the current checkout price rather than relying on an old published figure.
Best Value
For practical preparation, build one small end-to-end project that includes access control, evaluation, logging, failure handling, and cost controls. A portfolio, architecture document, code sample, or practical microcredential may provide more hiring evidence than stacking a second exam with little hands-on work.
Alternatives to AWS
Choose based on the cloud ecosystem used by your target employers, not simply on exam price.
Google Cloud Professional Machine Learning Engineer is an active alternative for teams using Google Cloud, Vertex AI, Gemini, and Google’s data platform. Google lists a $200 exam fee plus applicable tax, a two-hour exam, 50–60 questions, and recommends at least three years of industry experience including one year designing and managing Google Cloud solutions. See the official Google Cloud page.
Microsoft Azure AI Engineer Associate should not be treated as a normal new-candidate recommendation because Microsoft marks the certification and renewal assessment as retired. Check Microsoft’s current status page for replacement pathways.
Bottom line
AWS is shifting its AI credentials from a traditional machine-learning focus toward a broader pathway covering AI literacy, production ML operations, foundation models, RAG, agents, and application engineering.
Choose AI Practitioner for foundational knowledge, MLA-C01 or MLA-C02 for ML engineering and MLOps, and Generative AI Developer – Professional for advanced production AI application development. Treat the exam fee as only one part of the investment, and pair any credential with demonstrable work rather than assuming the certificate alone guarantees a job, promotion, or salary increase.
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