AWS Certified AI Business Strategist (AIB-C01)
The AWS Certified AI Business Strategist (AIB-C01) exam is intended for individuals who evaluate, champion, or scale AI initiatives in their organizations or for their clients. The exam validates a candidate's ability to translate AI capabilities into business outcomes, establish responsible AI practices, and drive AI adoption at scale.
Topics
Introduction
The AWS Certified AI Business Strategist (AIB-C01) 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. The exam covers AI strategy, governance, and organizational transformation grounded in foundational AI literacy. The exam prioritizes strategic decision-making over technical implementation.
The exam also validates a candidate's ability to complete the following tasks:
Apply foundational AI concepts and terminology to business contexts.
Select appropriate AI solution types based on business requirements and constraints.
Use generative AI (GenAI) techniques, including prompt engineering and model adaptation, to achieve business outcomes.
Develop and prioritize AI strategies that align with organizational objectives.
Measure AI business value using key performance indicators (KPIs), return on investment (ROI) frameworks, and baseline metrics.
Position AI initiatives for competitive advantage and business model transformation.
Apply responsible AI principles when making business decisions that require tradeoffs.
Establish governance structures and ensure regulatory compliance for AI initiatives.
Identify enterprise AI risks and direct appropriate mitigation strategies.
Assess organizational AI readiness and maturity across people, processes, technology, and governance.
Evaluate data and infrastructure foundations required to support AI initiatives.
Lead enterprise-wide change management and build AI-ready workforce capabilities.
Scale AI initiatives from pilots to enterprise-wide deployments by using iterative approaches.
Target candidate description
The target candidate is a business professional who evaluates, champions, or scales AI initiatives in their organization or for their clients. The target candidate works alongside technical teams but does not build AI solutions themselves. Typical roles include product managers, program managers, sales professionals, line-of-business managers, consultants, marketers, and business analysts. No coding or hands-on AWS implementation experience is required. The target candidate should have a basic familiarity with AI concepts, and a general awareness of what AWS AI services offer at a business level (without needing hands-on experience with the services). A baseline of 6 months of experience working with or alongside teams adopting AI is recommended. These professionals are expected to recognize common categories of AI tools and their business applications, such as recommendation engines, natural language processing (NLP), computer vision, document extraction, and AI for customer operations, without needing to configure or build these solutions.
Recommended AWS knowledge
The target candidate should have familiarity with AWS AI and machine learning (ML) service offerings at a strategic level, including their business applications. Technical implementation expertise is not required.
Familiarity with the following services at a high level:
Amazon Bedrock (generative AI [GenAI] platform, pricing tiers, Guardrails, Knowledge Bases)
Amazon SageMaker AI (custom ML and when to use managed or custom solutions)
Amazon Quick (AI-powered business assistants)
Familiarity with AWS frameworks and guidelines that support responsible, scalable AI adoption:
AWS Cloud Adoption Framework (AWS CAF) for planning and scaling AI initiatives across an organization
AWS shared responsibility model for AI workloads, including data security and compliance at a governance level
AWS Well-Architected Framework (Responsible AI Lens) for governance best practices
Familiarity with AWS tools that support business case development and return on investment (ROI) analysis:
AWS AI service pricing structures (for example, consumption-based, instance-based, seat-based)
Cost optimization strategies (for example, Savings Plans)
AWS Pricing Calculator and AWS Cost Explorer for cost planning
AWS Marketplace for evaluating build-buy-partner decisions
Job tasks that are out of scope for the target candidate
The following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. The following tasks are out of scope for the exam:
Developing or coding AI and ML models or algorithms
Implementing data engineering or feature engineering techniques
Performing hyperparameter tuning or model optimization
Building and deploying AI and ML pipelines or infrastructure
Conducting mathematical or statistical analysis of AI and ML models
Implementing security or compliance protocols for AI and ML systems
Configuring, deploying, or administering AWS services or cloud infrastructure
Selecting or tuning specific algorithms, frameworks, or technical architectures for AI solutions
Performing hands-on data pre-processing, cleaning, labeling, or annotation
Managing technical operations of AI systems in production (for example, monitoring infrastructure, debugging pipelines, troubleshooting model performance)
Exam content
Question types
The exam contains one or more of the following question types:
Multiple choice: Has one correct response and three incorrect responses (distractors).
Multiple response: Has two or more correct responses out of five or more response options. You must select all the correct responses to receive credit for the question.
Unanswered questions are scored as incorrect. There is no penalty for guessing.
Exam duration
You will have 130 minutes to complete your exam.
Exam results
The AWS Certified AI Business Strategist (AIB-C01) exam has a pass or fail designation.1 The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.
Note
1 For additional information on beta testing, see Before Testing policies
Your results for the exam are reported as a scaled score of 100–1,000. The minimum passing score is 700. Your score shows how you performed on the exam as a whole and whether you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.
Your score report could contain a table of classifications of your performance at each section level. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.
Each section of the exam has a specific weighting, so some sections have more questions than other sections have. The table of classifications contains general information that highlights your strengths and weaknesses. Use caution when you interpret section-level feedback.
Content outline
This exam guide includes weightings, content domains, tasks, and skills for the exam. This guide does not provide a comprehensive list of the content on the exam.
The exam has the following content domains and weightings:
Content Domain 1: AI Fundamentals and Literacy (24% of scored content)
Content Domain 2: AI Strategy and Business Value Creation (28% of scored content)
Content Domain 3: AI Governance and Responsible AI Leadership (24% of scored content)
Content Domain 4: Business Readiness, Leadership, and AI Transformation (24% of scored content)
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