AWS · AIB-C01
Validates a business professional's ability to translate AI capabilities into business outcomes and establish responsible AI practices. Targets non-technical roles who drive AI strategy, governance, and organizational transformation without requiring coding or AWS implementation experience.
Practice Questions
341
≈ 5 practice exams
Duration
170 minutes
Passing Score
700/1000
Difficulty
BusinessLast Updated
Sep 2026
Use this AIB-C01 practice exam to prepare for AWS Certified AI Business Strategist (AIB-C01) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 341 questions for AWS AIB-C01, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as AI Fundamentals and Literacy, AI Strategy and Business Value Creation, AI Governance and Responsible AI Leadership, and Business Readiness, Leadership, and AI Transformation. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The AWS Certified AI Business Strategist (AIB-C01) is a business-category certification that validates a business professional's ability to translate AI capabilities into measurable business outcomes, establish responsible AI practices, and drive AI adoption at enterprise scale. The exam covers four core areas: AI fundamentals and literacy, AI strategy and business value creation, AI governance and responsible AI leadership, and business readiness and organizational transformation. It emphasizes strategic decision-making over technical implementation, making it distinct from AWS's more technically oriented AI certifications. Candidates are expected to demonstrate fluency in AI and generative AI (GenAI) concepts, including prompt engineering, model adaptation techniques such as Retrieval Augmented Generation (RAG), and AI agent capabilities—all applied in business contexts rather than engineering ones. The exam guide expects high-level awareness of AWS AI and ML services and governance frameworks, but not hands-on AWS implementation or service administration.
This certification is designed for business professionals who evaluate, champion, or scale AI initiatives within their organizations or for clients. Typical roles include product managers, program managers, sales professionals, line-of-business managers, consultants, marketers, and business analysts—professionals who work alongside technical teams but do not build AI solutions themselves. No coding or hands-on AWS implementation experience is required. Candidates should have at least 6 months of experience working with or alongside teams adopting AI, along with a basic familiarity with AI concepts and a general awareness of what AWS AI services offer at a business level.
There are no formal prerequisites for this exam. AWS recommends that candidates have a baseline of 6 months of experience working with or alongside teams that are adopting AI. Candidates should have a basic familiarity with AI concepts and terminology (such as algorithms, models, training, and inference) and a general awareness of AWS AI and ML service offerings at a strategic level—without requiring hands-on experience. Familiarity with business frameworks for AI governance, such as the AWS Cloud Adoption Framework, the AWS shared responsibility model, and the AWS Well-Architected Framework's Responsible AI Lens, is also beneficial. Candidates should be comfortable understanding ROI frameworks, cost planning tools like AWS Pricing Calculator and AWS Cost Explorer, and build-buy-partner evaluation criteria for AI initiatives.
The currently bookable AIB-C01 beta exam has 85 multiple-choice and multiple-response questions, a 170-minute time limit, and a $50 USD beta price. The published standard exam guide specifies a 130-minute exam scored from 100–1,000 with a minimum passing score of 700; AWS lists the standard Business-category price as $100 USD. Multiple-choice questions have one correct response and three distractors, while multiple-response questions require selecting two or more correct answers from five or more options. Unanswered questions are incorrect and there is no guessing penalty. AWS uses compensatory scoring, so candidates need to pass the exam overall rather than every domain separately. Delivery is available online or at Pearson VUE testing centers.
The AWS Certified AI Business Strategist credential positions holders to lead and govern AI initiatives within their organizations without requiring a technical background, making it uniquely valuable for business leaders, consultants, and managers seeking to bridge the gap between AI capabilities and business outcomes. Roles that benefit directly from this certification include AI program manager, digital transformation lead, AI strategy consultant, product manager for AI-driven products, and line-of-business AI champion. As organizations accelerate AI adoption, demand for professionals who can evaluate AI investments, govern responsible AI practices, and scale initiatives enterprise-wide is growing rapidly. This certification complements the AWS Certified AI Practitioner (AIF-C01) but is distinct in its non-technical, strategy-first orientation—making it the preferred credential for business professionals rather than engineers. Early adopters who certify before February 15, 2027 receive an exclusive Early Adopter digital badge, signaling pioneering status in this emerging credential category.
5 sample questions with answers and explanations. The full bank has 341 questions, enough for 5 full-length practice exams.
Preview — answers shown1. A support center saves 300 employee hours monthly at a loaded cost of $30 per hour and gains $4,000 in monthly revenue contribution. Monthly operating cost is $7,000. What is the approximate monthly ROI? (Select one!)
Explanation
Monthly benefit is the value of saved time plus revenue contribution: 300 × $30 + $4,000 = $13,000. Net benefit is $13,000 − $7,000 = $6,000, and monthly ROI is $6,000 ÷ $7,000, or approximately 86%. The other percentages omit part of the benefit or use the wrong denominator, so they do not represent the stated ROI formula.
2. A retailer wants to forecast next month’s demand for each product using historical sales and labeled outcomes. Which term describes the learned artifact that produces the forecasts? (Select one!)
Explanation
A model is the learned result that applies patterns to new inputs and produces outputs such as forecasts. An algorithm is the method used to learn those patterns, training data supplies historical examples, and inference is the act of applying the trained model to new data. The forecast itself is a prediction, not the learned artifact.
3. A support center expects an AI assistant to save 300 hours monthly at a loaded cost of $30 per hour and produce $4,000 in monthly gross-margin revenue lift. Monthly operating cost is $7,000. What is the monthly ROI? (Select one!)
Explanation
Monthly benefit is labor value of $9,000 plus $4,000 in revenue lift, or $13,000. Net benefit is $13,000 minus $7,000, which equals $6,000. Dividing $6,000 by the $7,000 monthly operating cost gives approximately 86%. The other percentages omit part of the benefit or use the wrong denominator, so they do not represent the stated ROI formula.
4. A customer-service pilot saves 300 employee hours monthly at a loaded cost of $30 per hour. It also creates $4,000 of monthly gross-margin benefit from additional sales. Monthly operating cost is $7,000. What is the approximate monthly ROI? (Select one!)
Explanation
Monthly benefit equals the labor cost savings plus additional gross margin: (300 hours × $30/hour) + $4,000 = $9,000 + $4,000 = $13,000. Monthly ROI is calculated as (Monthly Benefit − Monthly Cost) ÷ Monthly Cost: ($13,000 − $7,000) ÷ $7,000 = $6,000 ÷ $7,000 ≈ 85.71%, or approximately 86%.
5. Before deploying an AI tool to reduce internal research time, which baseline should the business establish? (Select one!)
Explanation
A useful baseline measures the pre-AI business process, including time, volume, quality, and opportunity cost, so later changes can be attributed more credibly. Vendor count and model size are not business outcomes. Forecast token usage and projected accuracy are future estimates, while employee interest is useful context but not a sufficient operating baseline.
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