The Short Answer
The AWS Certified AI Business Strategist (AIB-C01) is a certification for people who decide what to do with AI, not for people who build it. It tests whether you can turn AI capabilities into business outcomes, build a business case and an ROI model, apply responsible AI and governance, and take an AI initiative from pilot to enterprise-wide adoption.
It is the first and so far the only exam in AWS's new Business certification category, which sits alongside Foundational, Associate, Professional, and Specialty. It is in beta right now. Beta delivery started on September 29, 2026, the beta costs $50 instead of the standard $100, and anyone who earns the certification by February 15, 2027 also gets an Early Adopter digital badge.
If you are a product manager, program manager, consultant, business analyst, marketer, salesperson, or business leader working with AI teams, AIB-C01 was written for you. If you want to show technical AI literacy and knowledge of AWS AI services, the AI Practitioner (AIF-C01) is still the better fit. The comparison below explains the difference.
What AWS Says the Exam Validates
In AWS's words, the exam "validates a business professional's ability to translate AI capabilities into business outcomes, establish responsible AI practices, evaluate opportunities to optimize AI solutions, and drive AI adoption at scale." It "prioritizes strategic decision-making over technical implementation."
In practice, questions are business scenarios. AWS's launch blog gives a typical example: a vendor proposes a $2M AI deployment, and you have to judge whether the investment is sound, premature, or missing critical success factors. AWS services appear as context in those scenarios, but they are not the subject being tested.
The official exam guide lists these skills:
- Apply foundational AI concepts and terminology to business contexts
- Select the right type of AI solution for the business requirements and constraints
- Use generative AI techniques, including prompt engineering and model adaptation, to reach business outcomes
- Develop and prioritize AI strategies that align with organizational objectives
- Measure AI business value with KPIs, ROI frameworks, and baseline metrics
- Position AI initiatives for competitive advantage and business model transformation
- Apply responsible AI principles when business decisions involve tradeoffs
- Establish governance structures and keep AI initiatives compliant with regulations
- Identify enterprise AI risks and direct mitigation strategies
- Assess organizational AI readiness and maturity across people, process, technology, and governance
- Evaluate the data and infrastructure foundations AI initiatives need
- Lead enterprise-wide change management and build an AI-ready workforce
- Scale AI initiatives from pilots to enterprise-wide deployments with iterative approaches
Who It Is For
The exam guide describes the target candidate as "a business professional who evaluates, champions, or scales AI initiatives in their organization or for their clients," someone who "works alongside technical teams but does not build AI solutions themselves."
Typical roles are product and program managers, sales and business development professionals, line-of-business leaders, consultants, business analysts, and marketers.
There are no prerequisites. AWS says outright that "no coding, AWS implementation experience, or other AWS certifications are required." AWS recommends about six months of experience working with or alongside teams that are adopting AI. You should be able to recognize common categories of AI tools and what they are used for, such as recommendation engines, natural language processing, computer vision, document extraction, and AI for customer operations, without needing to configure them.
The guide also lists what you are explicitly not expected to do: write or train models, do data or feature engineering, tune hyperparameters, build ML pipelines, configure or administer AWS services, choose algorithms or architectures, or troubleshoot AI systems in production. If those tasks are your job, look at the AI Practitioner, ML Engineer Associate, or Generative AI Developer Professional instead.
Exam Format at a Glance
These are the facts AWS has published so far. Some details of the standard (post-beta) exam have not been announced yet, and the table marks them.
| Item | AIB-C01 |
|---|---|
| Full name | AWS Certified AI Business Strategist |
| Exam code | AIB-C01 |
| Category | Business (new category; AIB-C01 is its only exam) |
| Status | Beta (delivery started September 29, 2026) |
| Beta questions | 85, multiple choice and multiple response |
| Beta time limit | 170 minutes |
| Standard exam time | 130 minutes (per the exam guide) |
| Standard question count | Not yet published by AWS |
| Scoring | Pass/fail, scaled 100–1,000, 700 to pass, compensatory |
| Price | $50 USD beta; $100 USD standard |
| Delivery | Pearson VUE test center or online proctored |
| Languages | English and Japanese in beta; more at general availability |
| Prerequisites | None |
| Recommended experience | 6 months working with or alongside AI initiatives |
| Valid for | 3 years |
How the Questions and Scoring Work
There are two question types. A multiple choice question has one correct answer and three distractors. A multiple response question has two or more correct answers out of five or more options, and you only get credit if you select all of the correct ones.
Unanswered questions count as wrong and there is no penalty for guessing, so never leave a question blank.
Scoring is compensatory. You need an overall scaled score of 700 or more, and you do not have to pass each domain separately. A strong result in strategy can make up for a weaker result in governance.
AWS policy says a beta exam contains more total questions and more scored questions than the standard version of the same exam. That is why the beta has 85 questions and 170 minutes, while the exam guide lists 130 minutes for the standard version.
The Four Domains and Their Weights
The exam has four content domains, 13 task statements, and 58 skills. Domain 2 is the largest, and the other three are weighted equally.
| Domain | Weight | What it covers |
|---|---|---|
| 1. AI Fundamentals and Literacy | 24% | AI vs ML vs generative AI, structured vs unstructured data, data quality, training and inference, rule-based automation vs AI, AI agents, model drift, shadow AI, prompt engineering, tokens and context windows, RAG vs fine-tuning, ISO/IEC 23053 and 42001 |
| 2. AI Strategy and Business Value Creation | 28% | High-impact use cases, build-buy-partner decisions, prioritizing (scale, pause, or stop) initiatives, when AI is the wrong answer, KPIs, baselines, ROI, leading indicators, cost controls, competitive advantage, investment levels |
| 3. AI Governance and Responsible AI Leadership | 24% | Responsible AI dimensions and tradeoffs, governance by design, human oversight and guardrails, cross-functional governance, regulatory compliance, access controls, risk classification, bias drift, harmful content, IP, hallucinations |
| 4. Business Readiness, Leadership, and AI Transformation | 24% | Readiness and maturity assessment, capability gaps, data strategy and silos, infrastructure foundations, executive sponsorship, change management, workforce AI literacy, envision-experiment-launch-scale phases, centers of excellence, pilot to production |
Which AWS Services You Need to Know (and Which You Don't)
AWS describes the in-scope services as "basic application only." You need to know what each one is for and when a business would choose it, not how to configure it.
AI and machine learning: Amazon Bedrock (the generative AI platform, its pricing tiers, Guardrails, and Knowledge Bases) and Amazon SageMaker AI (custom ML, and when a managed service beats a custom build).
Business intelligence: Amazon Quick, AWS's AI-powered business assistant.
Strategy and governance: the AWS Cloud Adoption Framework (AWS CAF) for planning and scaling AI across an organization, the AWS shared responsibility model applied to AI workloads, and the Responsible AI Lens of the AWS Well-Architected Framework.
Pricing and cost: AI pricing structures (consumption-based, instance-based, and seat-based), Savings Plans, the AWS Pricing Calculator, AWS Cost Explorer, and AWS Marketplace for build-buy-partner decisions.
Everything else is out of scope, including compute, networking, databases and storage, containers, developer tools, and security configuration such as writing IAM policies or managing KMS keys. If you are coming from a technical AWS certification, unlearn the urge to pick the most elegant architecture. The right answer here is the one that makes the best business decision.
Beta Details: Dates, Price, and the Early Adopter Badge
Beta registration opened on September 1, 2026, and the first beta exams were delivered on September 29, 2026. AWS has not published an end date for the beta or a general availability date. Its policy says beta exams generally run for one to three months.
The beta costs $50 USD, half the $100 standard price. If you pass the beta, you earn the full certification, valid for three years, the same as if you had passed the standard exam.
Earn the certification by February 15, 2027 and you also receive an Early Adopter digital badge in addition to the regular credential badge.
Before you book the beta, know three things:
You can take the beta only once. If you fail, you have to wait for the standard version to retake it.
Results take up to five business days. AWS's policy gives results for all exams, beta included, within five business days.
There is no official practice exam during the beta. AWS's Official Practice Exam for AIB-C01 is "not available during beta," although the Skill Builder exam prep plan and the Official Practice Question Set are.
Does the Pearson VUE 50% Off + Free Retake Offer Apply?
No, not while AIB-C01 is in beta. Pearson VUE's Global Retake offer gives 50% off your first attempt at a participating AWS exam and one free retake if you fail, for first attempts taken between September 29 and November 17, 2026. The offer explicitly excludes beta exams.
The beta is already priced at $50, which is the same amount you would pay with the offer on a $100 Foundational exam. You just don't get the free retake, and AWS's beta policy rules out a second beta attempt anyway. If you want the discount and the safety net for another exam, such as the AI Practitioner, which does participate, the details are on our discounts page.
AIB-C01 vs AIF-C01: How They Compare
AWS sums up the difference in one line: "AI Practitioner certification validates technical understanding. AI Business Strategist certification validates business judgment." The AI Business Strategist "does not assess knowledge of AWS AI services," while the AI Practitioner validates "foundational knowledge of AI, ML, generative AI concepts, and AWS AI services." Neither is a prerequisite for the other.
| Factor | AI Business Strategist (AIB-C01) | AI Practitioner (AIF-C01) |
|---|---|---|
| Category | Business | Foundational |
| Status | Beta since September 29, 2026 | Generally available since 2024 |
| Questions | 85 in beta; standard count not yet published | 65 |
| Time limit | 170 min beta; 130 min standard | 90 minutes |
| Passing score | 700/1000 | 700/1000 |
| Cost | $50 beta; $100 standard | $100 |
| Core question | Should we do this, and how do we make it pay off safely? | What is this AI concept, and which AWS service does it? |
| Domains | Fundamentals 24%, Strategy and Value 28%, Governance 24%, Readiness and Transformation 24% | AI/ML Fundamentals 20%, GenAI Fundamentals 24%, Foundation Model Applications 28%, Responsible AI 14%, Security and Governance 14% |
| AWS services | Bedrock, SageMaker AI, Quick, CAF, and pricing tools at a strategic level | Bedrock, SageMaker, Rekognition, Comprehend, Lex, Polly, Textract, Transcribe, Kendra, Personalize |
| Business topics | ROI, KPIs, build-buy-partner, change management, maturity models, AI centers of excellence | Few or none |
| Coding required | No | No |
| Best for | Product, program, sales, consulting, marketing, and business leaders | Anyone who needs technical AI literacy, including future ML engineers and developers |
Where the Two Exams Overlap
The overlap is real but smaller than the names suggest. Both exams expect you to tell AI, ML, and generative AI apart, to understand prompt engineering, to know when RAG fits and when fine-tuning does, and to recognize hallucinations, bias, and the core responsible AI dimensions such as fairness, explainability, privacy, safety, and transparency.
They ask about these topics differently. An AIF-C01 question about RAG usually asks which Bedrock feature or technique solves a technical problem. An AIB-C01 question about RAG is more likely to ask whether grounding a model in company documents is a better investment than fine-tuning, given the budget, the timeline, and how often the data changes.
Most of AIB-C01 has no counterpart in AIF-C01: building a business case, setting baselines before launch, deciding to scale, pause, or stop a pilot, designing a governance board, running change management, and building a center of excellence. That covers most of Domains 2 and 4 and the governance-structure half of Domain 3.
Which Should You Take First?
It depends on what you do day to day and where you want to go next.
Take AIB-C01 first if you are in a business role and AI is something you lead, sell, or fund rather than build. The certification maps directly to your job, it does not require AIF-C01, and two deadlines favor going now: the $50 beta price only lasts as long as the beta, and the Early Adopter badge needs a pass by February 15, 2027.
Take AIF-C01 first if you are new to AI terminology, if you are technical or heading that way, or if you plan to go on to the ML Engineer Associate or Generative AI Developer Professional. AWS's own guidance points technical learners from AI Practitioner to those two exams. AIF-C01 also gives you most of the vocabulary for AIB-C01's Domain 1, which is 24% of the exam.
Take both if you sit between business and technology, as solutions consultants, technical product managers, and pre-sales engineers often do. AWS says you "have the option to earn both if you want to demonstrate both technical AI knowledge and strategic AI business judgment." If you have the time before February 15, 2027, a reasonable order is AIF-C01 now (it is covered by the Pearson VUE offer until November 17, 2026), then AIB-C01 while it is still in beta.
If you are completely new to cloud, the AWS Certified Cloud Practitioner is still the broadest foundation, but you do not need it for either AI exam.
How to Prepare
AIB-C01 rewards judgment more than memorization, so prepare by working through scenarios rather than lists of services.
Start with the exam guide and our certification guide, which summarizes each of the four domains. Weight your time toward Domain 2 (28%), and make sure you can explain ROI, KPIs versus baselines versus leading indicators, and the build-buy-partner decision without notes.
Learn the frameworks AWS keeps coming back to: the AWS CAF phases, the responsible AI dimensions, AI risk classification, and AI maturity models. Know them well enough to apply them, not just to name them.
Then practice on questions. Our free AIB-C01 practice exam uses scenario questions in the beta's format, multiple choice and multiple response, and shows which domains you are weak in. Our cheat sheet condenses the four domains, the in-scope services, and the key frameworks into a 14-page PDF for review in the last days before the exam.
On the official side, AWS Skill Builder has an exam prep plan, an Official Practice Question Set, and a set of digital courses for the AI Business Strategist role. The Official Practice Exam is not available during the beta.
If you prefer a video course, Stephane Maarek's Ultimate AWS Certified AI Business Strategist course on Udemy walks through all four domains. For more question practice after ours, Abhishek Singh's AIB-C01 practice exams on Udemy add further full-length sets.
Read each scenario for the constraint that decides it: budget, timeline, regulation, data readiness, or how mature the organization is. Answers that are technically impressive but ignore that constraint are usually the distractors.