AWS Certified AI Business Strategist (AIB-C01)

Who this cert is for

AWS positions this exam for professionals who evaluate, champion, or scale AI initiatives — product and program managers, consultants, marketers, sales professionals, line-of-business managers, and business analysts. You work alongside technical teams; you do not need to build models or configure AWS services yourself.

Recommended baseline: basic familiarity with AI concepts, general awareness of what AWS AI services offer at a business level, and about six months working with or alongside AI adoption efforts.

What the exam validates

• Apply foundational AI concepts to business contexts
• Select appropriate AI solution types given requirements and constraints
• Use GenAI techniques (including prompt engineering and model adaptation) toward business outcomes
• Develop and prioritize AI strategies aligned to organizational objectives
• Measure value with KPIs, ROI frameworks, and baselines
• Apply responsible AI principles when tradeoffs are required
• Establish governance and compliance structures; identify enterprise AI risks
• Assess readiness across people, process, technology, and governance
• Lead change and scale from pilots to enterprise deployments

Content domains (official weightings)

Domain 2 is the largest: use-case selection, build/buy/partner, prioritization, KPIs, baselines, ROI, competitive advantage, and business-model impact.

Format notes (verify before booking)

• Standard exam guide: 130 minutes; multiple choice and multiple response; scaled score 100–1000; pass at 700.
• Beta offerings (when AWS runs them) have historically used different duration, question count, and price — confirm on the official certification page.
• Compensatory scoring: you need to pass overall, not every section.

AWS knowledge at a strategic level

You are expected to recognize business applications of services such as Amazon Bedrock (including Guardrails and Knowledge Bases at a conceptual level), Amazon SageMaker AI (when managed vs custom fits), and AI-powered business assistants. Frameworks worth knowing by name: AWS Cloud Adoption Framework (CAF) for AI planning, shared responsibility for AI workloads, and Well-Architected guidance for responsible AI. Cost tools (Pricing Calculator, Cost Explorer, Savings Plans concepts, Marketplace for build/buy/partner) support ROI conversations.

Out of scope (do not over-study these)

Coding models, feature engineering, hyperparameter tuning, pipeline/infrastructure build-out, statistical model analysis, hands-on security configuration, and day-to-day ops debugging are explicitly out of scope for the target candidate.

Suggested study plan (2–4 weeks)

1. Read the official exam guide end-to-end and list every task statement you cannot explain in one sentence.
2. Domain 1: AI vs ML vs GenAI; common business use cases (NLP, CV, recommendations, document extraction, customer ops); high-level ML lifecycle.
3. Domain 2: Practice writing a one-page business case: problem, baseline metric, AI approach, cost drivers, leading indicators, kill criteria.
4. Domain 3: Responsible AI principles, risk classification, governance roles, regulatory awareness (conceptual).
5. Domain 4: Readiness across people/process/tech/governance; change management; pilot → scale patterns.
6. Practice: AWS Skill Builder official practice question set and any Meeting Simulator materials AWS lists on the prep plan.

Pair with the checklist

If you champion AI investments at work, the AI Business-Case Checklist (paid PDF, checkout coming soon) mirrors Domain 2 decision hygiene: baselines, ROI, risk, and go/no-go.

Official resources

• AWS Certified AI Business Strategist (product page)
• Official AIB-C01 exam guide (docs)
• AWS Skill Builder exam prep plan (linked from the product page)