AWS has announced a new AI certification, AWS Certified AI Business Strategist (AIB-C01), currently in beta. The first question most people ask is how it differs from AI Practitioner.
In this article, I'll go through what the exam actually covers, then map out how it sits alongside the three existing AI certifications — AIF, MLA, and AIP.
Update: my prep course for this exam is out (beta edition)
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🎥 YouTube version (part of the course — practice tests and labs not included)
1. Exam basics
| Item | Detail |
|---|---|
| Full name | AWS Certified AI Business Strategist |
| Exam code | AIB-C01 |
| Level | Business |
| Status | Beta |
| Price | 50 USD (beta price / 100 USD at general availability) |
| Duration | 170 minutes (beta) — the exam guide states 130 minutes |
| Questions | 85 (beta) |
| Passing score | 700 / 1000 (compensatory scoring — no per-domain cutoff) |
| Languages | English, Japanese |
| Delivery | Pearson VUE test center / online proctored |
The first thing to note is the level: Business. It is not Foundational, Associate, Professional, or Specialty — it's a new tier.
Passing during the beta period (through February 15, 2027) earns an Early Adopter badge.
Beta exams normally include unscored validation questions, so the 130-minute figure is likely what the general-availability version settles on.
Domains and weighting
- Domain 1: AI Fundamentals and Literacy — 24%
- Domain 2: AI Strategy and Business Value Creation — 28%
- Domain 3: AI Governance and Responsible AI Leadership — 24%
- Domain 4: Business Readiness, Leadership, and AI Transformation — 24%
The heaviest domain is AI strategy and business value creation, where KPI design, ROI frameworks, and baseline metrics dominate.
In-scope AWS services
In scope
| Category | Services and elements |
|---|---|
| AI / ML (basic application only) | Amazon Bedrock, Amazon SageMaker AI |
| Business intelligence (basic application only) | Amazon Quick |
| Cloud strategy and governance | AWS Cloud Adoption Framework (AWS CAF), AWS shared responsibility model |
| Pricing and cost management | AWS AI service pricing structures (consumption-based / instance-based / seat-based), AWS Cost Explorer, AWS Marketplace, AWS Pricing Calculator |
Out of scope
| Category | Examples |
|---|---|
| Infrastructure and compute | Amazon EC2, AWS Lambda |
| Networking and content delivery | Amazon VPC, Amazon CloudFront |
| Databases and storage | Amazon RDS, Amazon S3 |
| Containers and orchestration | Amazon ECS, Amazon EKS |
| Developer tools and DevOps | AWS CodePipeline, AWS CloudFormation |
| Security implementation and configuration | IAM policy writing, AWS KMS |
| IoT, media, and other specialized services | — |
Four categories, and two of them carry a "basic application only" qualifier. The rest is pricing and frameworks.
What is explicitly not tested
Separately from services, the guide lists out-of-scope job tasks:
- Developing or coding AI and ML models or algorithms
- Implementing data engineering or feature engineering techniques
- Performing hyperparameter tuning or model optimization
- Building and deploying AI and ML pipelines or infrastructure
- Conducting mathematical or statistical analysis of AI and ML models
- Implementing security or compliance protocols for AI and ML systems
- Configuring, deploying, or administering AWS services or cloud infrastructure
- Selecting or tuning specific algorithms, frameworks, or technical architectures
- Performing hands-on data pre-processing, cleaning, labeling, or annotation
- Managing technical operations of AI systems in production
The certification page states that the exam does not assess knowledge of AWS services. Implementation, operations, and analysis are all out of scope. What is tested is whether you understand AI services at a strategic level and can make business decisions about them.
The only recommended prerequisite is six months of experience with AI initiatives. Coding experience and AWS implementation experience are explicitly not required.
2. Keywords from the exam guide
The guide breaks the four domains into 13 tasks. Pulling the keywords out of each task statement:
Domain 1: AI Fundamentals and Literacy
AI / ML / GenAI distinctions, structured vs. unstructured data, data quality, ISO/IEC 23053 and 42001, rule-based automation vs. AI, AI agents (autonomy, tool use, agent-to-agent communication, orchestration), model drift, shadow AI, prompt engineering, token limits and context windows, RAG, fine-tuning
Domain 2: AI Strategy and Business Value Creation
Use-case identification, build-buy-partner decisions, prioritization (scale, pause, or terminate), recognizing when AI is not the answer, KPIs (tangible and intangible), baseline metrics, ROI calculation, leading indicators, cost controls, competitive landscape assessment, business-model transformation, investment levels
Domain 3: AI Governance and Responsible AI Leadership
Responsible AI dimensions (fairness, explainability, privacy, safety, transparency, robustness), tradeoffs against business objectives, governance by design, human oversight, hallucination detection, guardrails, escalation criteria, cross-functional governance structures, regulatory compliance, access controls, AI risk classification frameworks, bias drift, harmful content, intellectual property
Domain 4: Business Readiness, Leadership, and AI Transformation
Readiness assessment, AI maturity models, capability gaps, data silos, data ownership, executive sponsorship, AI champions, cross-functional teams, change management, cultural barriers, POC programs and hackathons, shifting human roles toward oversight, envision / experiment / launch / scale, AI centers of excellence, moving from experiment to production-grade
Technical vocabulary appears, but what's tested is judgment, not implementation. RAG and guardrails show up as "when do you need this," not "how do you build it."
The governance, CoE, and change-management vocabulary in domains 3 and 4 has no precedent in previous AWS certifications.
3. How AIF, MLA, AIP, and AIB divide up
| Certification | Audience | Core services | In one line |
|---|---|---|---|
| AIF (AI Practitioner) | Everyone working with AI | Broad and shallow | Share a common AI vocabulary |
| MLA (ML Engineer – Associate) | ML engineers | SageMaker | Understand classical ML |
| AIP (Generative AI Developer – Professional) | GenAI developers | Bedrock | Build GenAI apps and agents |
| AIB (AI Business Strategist) | Business side | Frameworks and pricing | Select and justify AI investments |
AIF vs. AIB
- AIF — what generative AI is, what hallucination means, how Bedrock differs from SageMaker. You learn the vocabulary and can hold a conversation with engineers.
- AIB — which AI resources and initiatives to select, how to measure ROI, how to govern, how to reach production. Decision-making and organizational leadership.
AIB vs. MLA / AIP
MLA covers SageMaker pipelines, feature engineering, deployment, and monitoring. AIP covers RAG, agents, evaluation, and guardrails on Bedrock. Both live squarely inside AIB's out-of-scope list, so the overlap is minimal.
Holding MLA or AIP therefore does not make AIB easier. The technically optimal answer and the defensible business decision are not the same answer.
Wrapping up
- AIB-C01 is the first AWS certification at the Business level
- Implementation knowledge is not tested; investment decisions, ROI, governance, and enterprise rollout are
- Where AIF / MLA / AIP measure technical depth, AIB measures selection and decision-making on a separate axis
- The beta runs through February 15, 2027, at 50 USD with an Early Adopter badge
I'll sit the exam and follow up with a detailed report.
References
- AWS Certified AI Business Strategist certification page
- Exam guide (AIB-C01)
- AWS Certified Generative AI Developer – Professional (AIP-C01) exam guide
About the author
Maruchin Tech — 12x AWS Certified | Cloud & AI for manufacturing and supply chain (AWS / Google Cloud / Azure) | Udemy instructor (100K+ students)
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