AWS has certified cloud engineers for more than a decade. The AWS Certified AI Business Strategist (AIB-C01) is the first credential it has built entirely around business judgment, and the first to sit in the Business category of the certification catalog. There is no coding on it, no service configuration, and no architecture to design. It asks whether you can decide which AI initiatives to fund, how to measure whether they worked, and how to govern them once they are running. AWS describes those skills as portable across organizations and industries, and says the exam does not assess AWS services knowledge.
Exam Quick Facts
The beta is live
Beta delivery began 29 September 2026, at test centers and online. AWS says beta exams generally stay open for one to three months before the standard version replaces them.
What that means for you right now:
- Price: $50 during the beta, against a standard price of $100.
- Early Adopter badge: anyone who earns the certification by 15 February 2027 also receives it.
- Format: 85 questions in 170 minutes. AWS's beta policy says a beta form has more total questions and more scored questions than the standard version.
- Results: within five business days, the same as standard exams.
- One attempt: you can take the beta version only once. A retake waits until the standard version is generally available.
- Official prep: AWS's Official Practice Question Set is on Skill Builder now; the Official Practice Exam is not offered during the beta.
Preporato's AIB-C01 practice tests cover all four domains at the published weights, and the free AIB-C01 sample questions let you try the question style before you book.
What AIB-C01 actually tests
The exam validates four things: translating AI capabilities into business outcomes, establishing responsible AI practices, evaluating opportunities to optimize AI solutions, and driving AI adoption at scale. All four are grounded in foundational AI literacy, and the exam prioritizes strategic decision-making over technical implementation.
That framing sounds soft until you sit a question. A typical item gives you a company, a constraint, and four defensible-looking options. One of them addresses the constraint the scenario actually states. The others are things a reasonable person might do that miss it.
Here is the shape of it. A utility processes 4,000 rebate applications a month. Eligibility depends on three published criteria. A vendor proposes a generative AI application to decide eligibility. The technically impressive answer is to adopt the AI application. The correct answer is a rules engine for the decision and AI only for reading the uploaded receipts, because the criteria are fixed and auditable while the receipts are the part with genuine variability.
Determining when AI is not the appropriate solution is an explicit exam skill. It appears in Domain 2, and it catches candidates from technical backgrounds more than any other single idea on the exam.
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They come from the AIB-C01 practice tests, and each option shows its explanation once you answer.
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The four domains
The exam has no dominant domain. That is unusual, and it changes how you prepare, because there is no section you can afford to skip.
Core Topics
- •Core AI, ML and generative AI concepts in business terms
- •Structured and unstructured data, and why data quality drives outcomes
- •When to use rule-based automation instead of AI
- •AI agents: autonomy, tool use, and how they differ from other AI tools
- •Model drift and the need for ongoing monitoring
- •Shadow AI and transparent tool classification
- •Prompt engineering, tokens and context windows
- •RAG and fine-tuning as model adaptation techniques
- •Global frameworks: ISO/IEC 23053 and ISO/IEC 42001
Skills Tested
Example Question Topics
- Which of three initiatives is generative AI rather than predictive ML?
- A legal team pastes 200-page contracts in and later clauses go missing. Why?
- An HR assistant gives outdated policy answers. RAG or fine-tuning?
Who this is for
AWS describes the target candidate as a business professional who evaluates, champions, or scales AI initiatives, works alongside technical teams, and does not build AI solutions. Typical roles are product managers, program managers, sales professionals, line-of-business managers, consultants, marketers, and business analysts.
The recommended background is about six months working with or alongside teams adopting AI, plus basic familiarity with AI concepts and a general awareness of what AWS AI services do at a business level. There are no prerequisites. You do not need another AWS certification to sit it.
What is explicitly out of scope
AWS lists job tasks the target candidate is not expected to perform, and they tell you what will not appear:
- Developing or coding AI and ML models
- Data engineering or feature engineering
- Hyperparameter tuning or model optimization
- Building and deploying pipelines or infrastructure
- Mathematical or statistical analysis of models
- Implementing security or compliance protocols
- Configuring, deploying or administering AWS services
- Selecting or tuning specific algorithms or architectures
- Hands-on data pre-processing, cleaning, labeling or annotation
- Managing technical operations of production AI systems
If you have been preparing by learning Bedrock API calls, you are studying for a different exam.
The AWS services you actually need
The in-scope AWS surface is unusually small. The exam guide marks the AI services "basic application only", and AWS's own FAQ says the exam does not assess AWS services knowledge, so expect services to appear as context inside business decisions.
In-scope AWS services and frameworks
| Service or framework | What you need to know |
|---|---|
| Amazon Bedrock | Managed access to foundation models for generative workloads. Pricing tiers, Guardrails, Knowledge Bases at a strategic level. |
| Amazon SageMaker AI | The platform for building and operating custom models on proprietary data. When a managed solution fits and when a custom one is warranted. |
| Amazon Quick | AI-powered business intelligence for non-technical users asking questions of their own data. |
| AWS Cloud Adoption Framework (CAF) | Structuring an adoption programme across business, people, governance, platform, security and operations perspectives. |
| AWS shared responsibility model | Which security duties belong to AWS and which stay with you for AI workloads. |
| AWS Well-Architected Responsible AI Lens | Design guidance for building AI workloads responsibly. |
| AWS AI pricing structures and Savings Plans | Consumption-based, instance-based and seat-based pricing, and committing to steady usage for a lower rate. |
| AWS Pricing Calculator | Modelling the cost of a proposed configuration at assumed volumes. |
| AWS Cost Explorer | Analyzing what has actually been spent, historically. |
| AWS Marketplace | Discovering and purchasing third-party AI products, which supports build-buy-partner decisions. |
Know what each service is for and stop there. A generative assistant answering from company documents points to Bedrock. A custom forecasting model on twelve years of proprietary order data points to SageMaker AI. Letting non-technical managers ask questions of sales data and get charts back points to Amazon Quick. The pricing tools matter for the same reason: a business case needs a cost model before deployment (Pricing Calculator) and a check against actual spend afterward (Cost Explorer). Spend the rest of your preparation on the decisions those scenarios ask you to make.
Try three exam-style questions now. The explanations show the level of reasoning the exam expects.
Three AIB-C01 questions
A retail chain is reviewing three initiatives. The first is a demand forecasting system trained on five years of store sales history. The second is an assistant that drafts product descriptions from a short brief. The third is a rules engine that routes any refund under 50 USD for automatic approval. Which of the three is generative AI?
Generative AI produces new artifacts such as text, images, audio or code in response to a prompt. Forecasting is predictive machine learning, and the refund router applies a published threshold with no learning at all. Placing each correctly matters because the three carry different cost profiles, oversight needs and failure modes.
A professional services firm is building the ROI case for a document review assistant. It calculated that consultants will save 12,000 hours a year and multiplied those hours by loaded hourly cost. The cost side includes licensing and implementation. Which adjustment most improves the credibility of the case?
The case has two gaps reviewers will find. Cost is understated because a production AI system carries recurring inference, monitoring and update costs beyond licensing and implementation. Benefit is overstated unless the firm says whether saved hours become billable work, reduced headcount, or absorbed slack.
A bank has deployed a model that recommends whether to approve small business loans. Declined applicants have a legal right to be told why. The model is accurate and well monitored, but the vendor describes its reasoning as proprietary and provides only a score with no factor breakdown. Which responsible AI dimension is most directly at risk?
Explainability is the ability to describe how a system reached a particular outcome in terms a person can act on. In lending it is a regulatory requirement rather than a preference, which makes an unexplainable score unusable no matter how accurate it is. This is why explainability requirements belong in vendor selection rather than post-deployment review.
Full AIB-C01 set: 6 timed exams, every answer explained, $19.99 one-time.
See the practice testsExam format and scoring
Questions come in two types. Multiple choice has one correct response and three distractors. Multiple response has two or more correct responses out of five or more options, and you must select all of them to get credit. There is no partial credit and no penalty for guessing, so leave nothing blank.
Results are reported on a scaled score of 100 to 1,000, and 700 passes. They arrive within five business days, beta included. The exam uses a compensatory model, which means you do not need to pass each domain individually. You need to pass overall. Your score report may include a table classifying performance by section, which is useful for a retake but should be read carefully because sections carry different question counts.
The exam is available in English and Japanese.
A four-week study plan
This exam rewards structured reading and scenario practice more than hands-on time, which makes it unusually predictable to prepare for.
Week 1: read the source. Work through the official exam guide and be able to restate each task statement in your own words. Every question on the exam traces to one of them. Pay particular attention to the technologies and concepts list, which is short and explicit about what can appear.
Week 2: fix the vocabulary. Training against inference. Model drift. Hallucination. RAG against fine-tuning. Agents against assistants against rule-based automation. Structured against unstructured data. These get tested directly, and they also appear inside longer scenarios where a loose grasp costs you the question. Add the responsible AI dimensions and be ready to reason about tradeoffs between them.
Week 3: practice scenarios. This is where the exam is won. Work full-length practice tests under time, then read every explanation including the ones for options you did not pick. The skill being trained is discrimination between several defensible answers, and it develops through volume.
Week 4: close the gaps. Use per-domain scoring to find your weakest area and go back to the task statements underneath it. Re-sit at least one full test in exam conditions to confirm your pacing.
Pacing
The standard form allows 130 minutes. Business scenarios take longer to read than definition questions, so budget under two minutes per item and flag rather than dwell. On the beta form you get 170 minutes for 85 questions, exactly two minutes per item.
The five mistakes that cost marks
Reaching for the most advanced solution. A recurring pattern presents a problem whose cause is a broken process, a missing integration, or a published rule. The sophisticated AI answer is the trap.
Treating governance as a launch checklist. Governance questions turn on when a control is applied and who is accountable. A review two weeks before launch cannot influence the data and design decisions that created the risk.
Quoting benefits with no baseline. A percentage improvement with no baseline is not evidence, and the exam scores it that way. Establish baselines before deployment, separate adoption indicators from outcome measures, and include recurring run costs in ROI.
Confusing activity with progress. Models deployed, users trained, documents generated. These are activity measures. The exam repeatedly asks for the outcome measure that would tell you whether the initiative was worth funding.
Missing the stated constraint. Read the scenario for what it actually says. A hospital wanting faster handover documentation where nurses retype into three systems has an integration problem, not an AI problem.
Frequently asked questions
Where to start
Read the exam guide first. It is short, it is free, and every question on the exam traces back to a task statement in it. Then take AWS's Official Practice Question Set on Skill Builder to see AWS's own question style, and test yourself against scenarios, because the gap between understanding a concept and choosing between four plausible applications of it is where this exam actually lives.
Preporato's AIB-C01 practice tests are six full-length exams, 390 questions total, written directly against the published task statements at the official domain weights of 24 / 28 / 24 / 24. Every option carries an explanation, including why each strong distractor falls short. Start with the free AIB-C01 sample questions to see the question style before you commit.