AIB-C01 tests business judgment about AI, and most of its questions come down to a small set of decision rules applied to a scenario. This sheet collects those rules by domain, together with the vocabulary and the AWS facts the official exam guide lists, for final-week review. For the full picture, read the AIB-C01 complete guide; for exam-style practice with explanations, work through the AIB-C01 practice questions.
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Exam facts (October 2026)
AIB-C01 beta and standard exam
| Detail | Beta (open now) | Standard |
|---|---|---|
| Questions | 85 | Not yet published |
| Time | 170 minutes | 130 minutes |
| Price | $50 | $100 |
| Passing score | 700 on a 100 to 1,000 scale | 700 on a 100 to 1,000 scale |
| Scoring | Compensatory: pass overall, not per domain | Compensatory |
| Question types | Multiple choice (one of four) and multiple response (two or more of five or more, all required) | Same |
| Guessing | No penalty; blanks count as wrong | Same |
| Attempts | One beta attempt; a retake waits for the standard version | Standard retake policy |
| Results | Within five business days | Within five business days |
| Delivery | Pearson VUE test center or online, in English or Japanese | Same |
| Validity | 3 years | 3 years |
| Early Adopter badge | If certified by 15 February 2027 | If certified by 15 February 2027 |
Test yourself before you review. Each card hides its answer until you choose.
Three quick AIB-C01 questions
An insurance company learns that staff in three departments paste customer emails into a free public chatbot to draft replies. Leadership wants to reduce the risk without losing the productivity benefit. What should the company do first?
The exam guide names this exact control: a transparent classification of AI tools that tells staff what is approved, what is blocked, and what is being evaluated, which is how you reduce shadow AI risk while keeping the benefit. Pairing it with an approved tool removes the reason people went around the rules. A stops the immediate leak but drives usage onto personal devices and throws away the value. C leaves a governance decision to individual judgment, which is the problem the scenario describes. D accepts the current exposure for an unknown length of time.
A claims department launched an AI assistant that triages incoming claims. The steering committee asks how to judge whether it is working. Which TWO measures show business outcomes? (Select TWO)
Outcome measures tell you whether the initiative changed the business result it was funded for, and both correct options compare against a baseline taken before launch, which is what makes the change attributable. Training counts, prompt volume, and feature releases (A, C, E) are activity measures: they show the assistant is being rolled out and used, and say nothing about whether claims move faster or land in the right place.
Three business units each run their own AI pilots. They hold overlapping vendor contracts, review risk in different ways, and cannot reuse each other's work. The CEO wants to scale AI across the company. What should come next?
The exam guide lists AI centers of excellence and cross-functional collaboration mechanisms as the way to support scaling: shared standards end the inconsistent risk reviews, reusable assets end the duplication, and an intake process lets the business keep proposing initiatives. A kills the momentum the pilots built. B keeps the duplication and inconsistent risk handling the scenario describes. D outsources accountability without fixing standards or reuse.
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Seven decision rules that settle most questions
- Fixed, auditable criteria call for rules; variable inputs call for AI. When a decision follows published criteria, automate it with rules and use AI only where inputs vary, such as reading documents or free text. (Skills 1.2.1, 2.1.4)
- Take the baseline first. Measure the current process before deploying, or no improvement claim survives review. (2.2.2)
- Judge outcomes. Handling time, error rate, revenue, and satisfaction show whether an initiative worked, while users trained and prompts sent only show that it ran. (2.2.1, 2.2.4)
- Count the run costs. A credible ROI includes inference, monitoring, and model updates, and says what happens to the hours saved. (2.2.3, 2.2.5)
- Govern by design. Responsible AI and risk review start at planning, when data and design choices can still change. (3.1.3)
- Match oversight to harm. The higher the cost of a wrong output, the earlier a person reviews it; low-risk outputs can be sampled. (3.1.4, 3.2.4)
- Win small, then scale. Short-term wins with success metrics, a center of excellence, and feedback loops come before enterprise rollout. (4.4.2 to 4.4.4)
Domain 1: AI Fundamentals and Literacy (24%)
AI, machine learning, generative AI, and rules
| Term | What it is | Business example |
|---|---|---|
| Artificial intelligence | The umbrella term for machines performing tasks that normally need human intelligence | Any of the rows below |
| Machine learning | Models that learn patterns from historical data to predict or classify | Predicting which customers will cancel next month |
| Generative AI | Models that create new text, images, audio, or code from a prompt | Summarizing support calls for agents |
| Rule-based automation | Fixed logic written by people, with no learning | Routing invoices over a set amount to a manager |
- Training and inference: training builds a model from historical data; inference is the model answering new inputs, and it is the cost that recurs in production.
- Structured and unstructured data: tables and fields on one side; documents, email, images, and audio on the other. Data quality caps every result, so incomplete, biased, or stale data produces confident wrong outputs.
- AI agents: they take actions in other systems on their own (autonomy and tool use), which separates them from assistants that only answer questions, and they can hand work to other agents under an orchestration strategy.
- Model drift: performance changes as real-world data moves away from the training data, so production AI needs monitoring and updates.
- Shadow AI: unapproved tools staff use anyway. The exam guide's remedy is a transparent classification of tools: approved, blocked, under evaluation.
- Prompt engineering basics: state the task, give context and an example, specify the output format, and set constraints.
- Tokens and context windows: long documents or conversations can exceed what a model can take in, and the overflow is lost; split the work, summarize, or retrieve only what is needed.
- Standards to recognize: ISO/IEC 23053 is a framework for AI systems that use machine learning, which gives teams a shared vocabulary; ISO/IEC 42001 is the international standard for an AI management system (policies, roles, risk assessment, continual improvement).
Improving AI responses for a business need
| Technique | Use it when | Watch for |
|---|---|---|
| Prompt engineering | The base model can already do the task with better instructions | Cheapest first step, so try it before the other two |
| Retrieval Augmented Generation (RAG) | Answers must come from current company documents, ideally with citations | Retrieval quality and document access controls |
| Fine-tuning | You need a consistent style, format, or domain behavior the base model lacks | Training cost, and retraining when the content changes |
Domain 2: AI Strategy and Business Value Creation (28%)
The largest domain. Most questions ask which initiative to fund, how to prove it worked, or when to stop.
Build, buy, or partner
| Option | Fits when | Trade-off |
|---|---|---|
| Buy (SaaS or an AWS Marketplace product) | The need is common and speed matters | Less differentiation and more vendor dependence |
| Build (for example on Amazon Bedrock or Amazon SageMaker AI) | The use case differentiates you or depends on proprietary data | Longer timeline, and it needs skills and a run budget |
| Partner (a consultancy or vendor co-build) | You need a custom result but lack the skills or capacity | Cost, and knowledge transfer has to be planned |
Weigh build, buy, or partner on budget, timeline, in-house capability, vendor proposals, and regulatory requirements.
- Use cases: map an AI capability to a specific business outcome in customer operations, sales and marketing, research and development, or software development.
- Prioritizing: rank by business value, feasibility, sustainability, and strategic alignment, then decide to scale, pause, or terminate each initiative.
- When AI is the wrong tool: the cause is a broken process or a missing integration, the criteria are fixed rules, there is too little usable data, or the decision must be explained in ways the model cannot support.
- Moving a process onto AI, or between AI platforms: plan for business continuity, cost implications, data readiness, and performance impact.
- KPIs: tangible benefits (cost reduction, revenue growth, handling time, error rate) and intangible ones (customer satisfaction, employee productivity). Set the baseline before launch.
- Leading indicators predict success before outcomes arrive: active use by the intended users, data readiness, a named executive sponsor, and pilot accuracy against the baseline.
- ROI: (total benefits minus total costs) divided by total costs. Benefits come from time savings, cost reduction, revenue growth, and productivity gains. Costs include licensing or build, integration, inference, monitoring, updates, training, and change management.
- Cost control: estimate with AWS Pricing Calculator before you build, check actual spend in AWS Cost Explorer after, match the pricing structure to usage (consumption-based, instance-based, or seat-based), and use Savings Plans for steady, predictable compute.
- Competitive advantage: proprietary data, AI built into core processes, and speed of learning are durable; a feature any competitor can buy off the shelf erodes quickly.
- Investment level: follows industry maturity and competitive dynamics. Where rivals already use AI at scale, waiting costs more; in an early market, smaller bets that build learning come first.
Domain 3: AI Governance and Responsible AI Leadership (24%)
The exam guide names six responsible AI dimensions: fairness, explainability, privacy, safety, transparency, and robustness. AWS's own responsible AI list adds controllability and governance and pairs veracity with robustness.
Responsible AI dimensions
| Dimension | What it means | Where it shows up in a scenario |
|---|---|---|
| Fairness | Considering impacts on different groups of stakeholders | Lending, hiring, and pricing; proxy variables such as postcode |
| Explainability | Understanding and evaluating why the system produced an output | Customers or regulators are entitled to reasons |
| Privacy and security | Obtaining, using, and protecting data and models appropriately | Customer records in prompts or training data |
| Safety | Preventing harmful output and misuse | Medical, financial, or physical consequences |
| Controllability | Mechanisms to monitor and steer the system | Agents that act without a person in the loop |
| Veracity and robustness | Correct outputs, even with unexpected or adversarial inputs | Hallucinations, edge cases, prompt attacks |
| Governance | Best practices across the AI supply chain, from providers to deployers | Vendor models inside your own product |
| Transparency | Letting people make informed choices about their engagement with AI | Disclosing AI use to customers and staff |
- Trade-offs: when a business goal conflicts with a principle, the best answer usually cuts the harm while keeping most of the value: human review of declines, an explainable model for regulated decisions, collecting less personal data.
- Human oversight: the safeguards the exam guide lists are hallucination detection, guardrails, and escalation criteria. On AWS, Amazon Bedrock Guardrails provides content and word filters, denied topics, PII redaction, prompt attack detection, and hallucination detection through contextual grounding and Automated Reasoning checks.
- Governance structure: a cross-functional group (business, technical, legal, compliance, risk) with a named, accountable owner for each AI system.
- Compliance and security: identify the regulatory risk in each AI-enabled process, and apply access controls and data security. Under the shared responsibility model, AWS secures the underlying infrastructure and managed services; you own your data, who can access it, and how outputs are used.
- Risk classification: tier AI systems by potential impact and apply controls in proportion. The EU AI Act is the best-known example, with prohibited, high-risk, limited-risk, and minimal-risk categories.
- Bias: it can enter at data collection, labeling, training, and deployment, and it can drift after launch, so monitoring continues in production.
- Harmful content and IP: check rights to training data, ownership of outputs, and vendor indemnities; filter harmful content before it reaches users.
- Reliability: hallucinations, data quality degradation, and model drift all call for monitoring and controls once a system is live.
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Domain 4: Business Readiness, Leadership, and AI Transformation (24%)
- Readiness: leadership alignment, data quality, cultural preparedness, technical infrastructure, and governance frameworks.
- Maturity: organizations move from experimentation toward enterprise-scale deployment. Invest in the gaps that block the next stage, across people, process, technology, and governance.
- Data foundations: data quality, accessibility, and silos decide what AI can do, and so do data ownership, a data strategy, and sharing agreements between teams.
- Leading change: an executive sponsor and named AI champions; cross-functional teams with clear accountability; open communication about timelines, expected outcomes, and changes to roles.
- Cultural barriers: risk aversion, resistance to change, and fear of failure, met with visible sponsorship, safe pilots, and honest communication.
- AI literacy: proofs of concept, hackathons, training programs, and responsible AI training.
- Human roles: move people from manual operations to oversight of and collaboration with AI, keeping the strengths people bring (critical thinking, empathy, creativity).
- Scaling: progress in iterative phases (the exam guide's example is envision, experiment, launch, scale), start with short-term wins, set up a center of excellence, track success metrics with feedback loops, and give production systems real support, monitoring, ownership, and budget.
- AWS Cloud Adoption Framework: four phases (Envision, Align, Launch, Scale) and six perspectives (Business, People, Governance, Platform, Security, Operations).
AWS services and tools in scope
AWS says the exam does not assess AWS services knowledge, and the exam guide lists the AI services for basic application only. Know what each one is for.
In-scope AWS services, frameworks, and tools
| Service or tool | Know it as |
|---|---|
| Amazon Bedrock | Managed access to foundation models from several providers for generative AI apps and agents, with Guardrails and Knowledge Bases (RAG) |
| Amazon SageMaker AI | Building, training, and deploying custom models on your own data |
| Amazon Quick | The AWS AI assistant for work: research, BI dashboards, and automation for business users |
| AWS Cloud Adoption Framework (CAF) | Planning and scaling adoption: four phases, six perspectives |
| AWS shared responsibility model | AWS secures the infrastructure; you own data, access, and use |
| AWS Well-Architected Responsible AI Lens | Governance and design guidance for responsible AI workloads |
| AI pricing structures | Consumption-based, instance-based, and seat-based |
| Savings Plans | A lower rate in exchange for committed, steady usage |
| AWS Pricing Calculator | Estimating cost before you build |
| AWS Cost Explorer | Analyzing actual spend after you run |
| AWS Marketplace | Buying third-party AI products, which supports build-buy-partner decisions |
Traps and the answers that beat them
Tempting answers that lose
| Tempting answer | Why it loses | Better answer |
|---|---|---|
| Adopt the most advanced AI option | The cause is a fixed rule, a broken process, or a missing integration | Fix the process or use rules, and apply AI only where inputs vary |
| Hold a governance review just before launch | Too late to change the data and design decisions behind the risk | Governance from planning onward, with named owners |
| Report a 30 percent improvement | Without a baseline the number proves nothing | Measure a baseline before launch, then compare |
| Count users trained and models deployed | Activity measures say nothing about business results | Outcome KPIs tied to the goal the initiative was funded for |
| Block every AI tool | Pushes usage into shadow AI and gives up the value | Classify tools as approved, blocked, or under evaluation |
| Pilot in every department at once | No early wins to learn from and no shared standards | Short-term wins first, then a center of excellence to scale |
Using this sheet in the final week
Read the seven decision rules each morning. Early in the week, take one full timed practice test; two days before the exam, take another. For every miss, find the task statement it maps to in the official exam guide and reread that section of this sheet. If a domain stays weak, the how to pass AIB-C01 guide has a plan for the last two weeks, and the AI Business Strategist vs AI Practitioner comparison helps if you are still choosing between the two exams.
Rehearse the scenarios under the clock
Six full-length AIB-C01 practice exams at the official 24/28/24/24 weights, with an explanation for every option and a score for each domain, so you know which rules still need work.
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Frequently asked questions
Sources:
- AWS Certified AI Business Strategist (AIB-C01) exam guide
- AWS Certified AI Business Strategist certification page
- AWS Certification policies: beta exams
- AWS responsible AI dimensions
- Amazon Bedrock Guardrails
- Amazon Quick
- An Overview of the AWS Cloud Adoption Framework
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