AWS Certified AI Business Strategist course
4 modules, one per exam domain. 52 lessons, each opening with its key concepts; 6 timed practice tests.
One purchase covers the lessons and the tests.
What does the AIB‑C01 course cover?
Every module is one of the 4 exam domains Amazon Web Services (AWS) publishes, in exam order, with the domain's share of the exam as its bar. AI Strategy and Business Value Creation and AI Fundamentals and Literacy carry the most at 28% and 24%. The exam runs 130 minutes for Multiple choice and multiple response. The beta form, delivered from September 29, 2026, runs 85 questions in 170 minutes; the exam guide sets the standard form at 130 minutes.. Open a module for its lessons; the practice tests close the course.
Speak about AI, machine learning and generative AI with the precision an executive audience expects, and know what the technology can and cannot do.
24% of the exam
AI, Machine Learning and Generative AI in Business Terms
The vocabulary the exam assumes you already own, defined by what each technique changes about a business process.
- 1.1
What AI, Machine Learning and Deep Learning Actually Mean
AI is the umbrella; machine learning is the subset that learns from data · Deep learning uses neural networks with many layers · Models produce probabilistic outputs rather than guaranteed answers · Training happens once per version; inference happens on every request · Narrow AI solves one task; general AI does not exist commercially
25 minHigh priority
- 1.2
Generative AI and Foundation Models
Foundation models are pretrained on broad data and adapted downstream · Generative AI creates new content instead of scoring existing content · Large language models predict the next token from context · Multimodal models accept and produce more than one media type · Pretraining cost sits with the provider; adaptation cost sits with you
25 minHigh priority
- 1.3
How Models Learn: Supervised, Unsupervised and Reinforcement Learning
Supervised learning needs labeled examples and predicts a known target · Unsupervised learning finds structure without labels · Reinforcement learning optimizes a reward signal through feedback · Labeling effort is usually the hidden cost of a supervised project · The learning type follows from the business question being asked
22 minCore topic
- 1.4
Rule-Based Automation Compared With AI
Rules are deterministic, auditable and cheap to run · AI handles variation and ambiguity that rules cannot enumerate · Rule maintenance cost grows with the number of exceptions · Many production systems combine rules with a model · Choose rules when the logic is stable, known and must be explainable
22 minHigh priority
Data and the AI Lifecycle
Where AI projects get their raw material, and the lifecycle a model moves through after it leaves the lab.
- 1.5
Structured and Unstructured Data
Structured data fits rows and columns with a fixed schema · Unstructured data covers text, images, audio and video · Semi-structured formats such as JSON carry their own schema · Generative AI unlocked the unstructured majority of enterprise data · Storage choice follows the data shape and the access pattern
20 minCore topic
- 1.6
Data Quality, Labeling and Representativeness
Accuracy, completeness, consistency, timeliness and relevance · A model inherits every bias present in its training data · Representativeness is about who and what the data covers · Label quality caps the ceiling of a supervised model · Data work usually consumes most of an AI project timeline
25 minHigh priority
- 1.7
The AI Project Lifecycle From Idea to Production
Framing, data, development, evaluation, deployment, monitoring · Evaluation criteria must be agreed before development starts · Deployment is a milestone in the middle of the lifecycle · Each stage has a different owner and a different failure mode · Iteration is expected; a first model is rarely the shipped model
25 minHigh priority
- 1.8
Model Drift, Monitoring and Retraining
Data drift is a change in inputs; concept drift changes the relationship · Model performance decays silently without monitoring · Monitoring covers quality, latency, cost and business outcome · Retraining cadence is a business decision with a cost attached · A rollback path is part of a responsible deployment plan
25 minHigh priority
Working With Foundation Models
The adaptation techniques a strategist has to price, sequence and govern.
- 1.9
Prompt Engineering for Business Outcomes
A prompt carries instruction, context, examples and output format · Zero-shot, few-shot and chain-of-thought prompting · Prompt changes are the cheapest lever available · Prompt templates belong under version control and review · Prompt injection is a security concern, not a quality concern
22 minHigh priority
- 1.10
Tokens, Context Windows and What They Cost
Models bill per input and output token · The context window bounds how much the model can consider at once · Longer context raises cost and latency together · Token volume scales with usage, so unit economics matter early · Caching and summarization reduce repeated token spend
22 minHigh priority
- 1.11
RAG Compared With Fine-Tuning
RAG retrieves current documents and grounds the answer in them · Fine-tuning changes model weights to teach style, format or domain · RAG suits knowledge that changes; fine-tuning suits behavior that repeats · RAG keeps source citations and access control possible · Continued pretraining is the heaviest and rarest option
28 minHigh priority
- 1.12
AI Agents, Autonomy and Tool Use
An agent plans, calls tools and observes results in a loop · Autonomy is a spectrum from suggestion to unattended action · Tool access is where an agent gains real business reach and real risk · Multi-step agents compound error unless checked · Approval gates set the boundary between advice and action
25 minHigh priority
- 1.13
Shadow AI and Classifying the Tools Employees Already Use
Shadow AI is unsanctioned tool use inside business workflows · The main exposures are data leakage, IP loss and unverified output · Classification separates sanctioned, restricted and prohibited tools · Discovery comes before policy; a ban with no alternative fails · A sanctioned path is the most effective control
22 minCore topic
Choose the right use cases, decide whether to build or buy, price the work honestly and prove the value with numbers a CFO will accept.
28% of the exam
Finding and Prioritizing AI Use Cases
How a portfolio of candidate use cases gets built, ranked and cut.
- 2.1
Where AI Value Actually Comes From
Cost reduction, revenue growth, risk reduction and speed · Value shows up in a process metric before it shows up in the P&L · Automation, augmentation and new capability are different bets · Volume times time saved times rate is the base of most cases · Value that cannot be measured cannot be defended at renewal
25 minHigh priority
- 2.2
Discovering Use Cases Across the Business
Start from painful processes rather than from available models · Workshops, process mining and frontline interviews surface candidates · Describe a use case as a job, a decision and a measurable outcome · Group candidates into themes to find reusable foundations · A backlog with no owner per item is a wish list
22 minCore topic
- 2.3
Prioritization Frameworks: Value, Feasibility and Risk
Score value, feasibility and risk on a common scale · Feasibility is mostly data availability and process stability · A value versus feasibility matrix separates quick wins from bets · Sequence early wins that fund and de-risk the harder cases · Risk-adjusted value beats raw value for portfolio decisions
28 minHigh priority
- 2.4
When AI Is Not the Right Answer
No data, no repeatable pattern, no AI · Deterministic and fully auditable requirements favor rules · Very low volume rarely repays the build and run cost · Unacceptable error cost without human review is a stop signal · Naming the non-AI alternative strengthens every proposal
25 minHigh priority
Build, Buy and Partner Decisions
Sourcing the capability, evaluating vendors and understanding what AI costs to run.
- 2.5
The Build, Buy and Partner Decision
Differentiation is the first test: build what makes you different · Buy for commodity capability and faster time to value · Partner when the capability is strategic but the skills are missing · Total cost of ownership includes run, monitor and retrain · Lock-in and exit cost belong in the decision
28 minHigh priority
- 2.6
Evaluating Vendors and AWS Marketplace
Evaluate on outcome, data handling, security posture and roadmap · Ask how your data is used for training and retention · Proof of concept criteria are agreed before the trial starts · AWS Marketplace centralizes procurement, billing and terms · Vendor viability matters as much as vendor features
25 minCore topic
- 2.7
The Cost Structure of AI: What You Actually Pay For
Build cost, run cost and change cost behave differently · Inference is a variable cost that scales with adoption · Data preparation and labeling are often the largest line item · People and governance cost is routinely omitted from cases · Pilot unit cost rarely survives contact with production volume
25 minHigh priority
- 2.8
Cost Planning and Optimization With AWS Tools
AWS Pricing Calculator estimates before you commit · AWS Cost Explorer shows where spend actually went · Tagging by use case makes AI spend attributable · Model choice, prompt size and caching are the main levers · Set budgets and alerts before opening usage to the business
25 minHigh priority
Measuring AI Value
Baselines, KPIs, ROI and the early signals that tell you a project is working.
- 2.9
Baselines and Why They Come First
A baseline is the current performance of the process without AI · Without a baseline, improvement cannot be attributed · Baselines are captured before the pilot begins · Control groups and holdouts strengthen the claim · Human performance is the honest comparison for most tasks
22 minHigh priority
- 2.10
Choosing KPIs for AI Initiatives
Model metrics, process metrics and business metrics are three layers · Accuracy alone almost never answers a business question · Every KPI needs an owner, a source and a cadence · Adoption rate is the KPI most often missing · Guardrail metrics catch value gained at another function's expense
25 minHigh priority
- 2.11
ROI Frameworks and the Business Case
ROI, payback period, net present value and total cost of ownership · Benefits split into hard savings, soft savings and avoided cost · Ranges and sensitivity beat a single confident number · Time to value matters as much as size of value · Finance signs off on the assumptions, not on the model
28 minHigh priority
- 2.12
Leading Indicators of AI Project Success
Leading indicators move before financial results do · Adoption, task completion and override rate are early signals · Data readiness and executive engagement predict delivery · A rising override rate points at trust or quality problems · Review leading indicators monthly, lagging ones quarterly
22 minCore topic
Competitive Positioning and Business Model Change
How AI changes the position of a company in its market, and when it changes the business model itself.
- 2.13
AI as Competitive Advantage and Where It Erodes
Access to models is not an advantage; everyone has it · Proprietary data, workflow integration and speed are durable · Advantage erodes as capability becomes commodity · Table stakes capability must still be matched · Compounding data feedback loops widen a lead over time
25 minCore topic
- 2.14
Business Model Transformation With AI
AI can change what is sold, how it is priced and who delivers it · Outcome-based and consumption pricing follow AI economics · New models need new metrics and new operating processes · Cannibalization is a decision to make deliberately · Transformation is a portfolio of bets with different horizons
25 minCore topic
Set responsible AI principles that survive contact with a deadline, build the governance that enforces them and classify the risk a regulator will ask about.
24% of the exam
Responsible AI in Practice
The principles, the tradeoffs between them and the bias that appears at every stage.
- 3.1
The Dimensions of Responsible AI
Fairness, explainability, privacy, safety, robustness and transparency · Controllability and governance make the principles operational · Each dimension needs a measurable test to mean anything · Principles apply across the lifecycle, not only at launch · The AWS Well-Architected Responsible AI Lens frames the review
25 minHigh priority
- 3.2
Navigating Tradeoffs Between Principles
Accuracy, fairness, privacy and speed pull against each other · Tradeoffs are business decisions that need a named owner · Document the choice, the reasoning and the review date · Context sets the priority order of principles · Refusing to choose is itself a choice with consequences
25 minHigh priority
- 3.3
Bias Across the AI Lifecycle
Bias enters through problem framing, data, modeling and use · Historical bias is preserved by accurate models · Proxy variables reintroduce protected attributes · Fairness definitions conflict and cannot all hold at once · Measure outcomes by segment or the bias stays invisible
25 minHigh priority
- 3.4
Transparency, Explainability and Disclosure
Explainability targets a decision; transparency targets the system · Different audiences need different depth of explanation · Model cards and system cards document intended use and limits · Disclosure that a user is interacting with AI is increasingly required · Interpretable models trade some accuracy for defensible decisions
25 minHigh priority
Governance Structures and Oversight
Who decides, who reviews, where a human stays in the loop and what stops a bad output.
- 3.5
Governance by Design
Controls are cheapest when designed in at the start · Intake, review gates and approval thresholds form the spine · Governance scales by tiering effort to risk · An inventory of AI systems is the first artifact · Governance that blocks everything gets routed around
25 minHigh priority
- 3.6
Cross-Functional Governance Bodies and Accountability
Legal, risk, security, data, business and HR all hold a piece · A steering body sets policy; a review board clears use cases · Accountability sits with a named business owner · RACI removes the gap between advice and decision · Escalation paths need a defined time limit
25 minHigh priority
- 3.7
Human Oversight and Escalation Criteria
Human in the loop, on the loop and out of the loop · Oversight level follows the cost of an error · Confidence thresholds route uncertain cases to people · Reviewers need time, training and authority to overrule · Automation bias makes rubber-stamp review a real failure mode
25 minHigh priority
- 3.8
Guardrails and Hallucination Safeguards
Guardrails filter input and output against defined policy · Grounding in retrieved sources reduces fabrication · Citations let a reviewer verify a claim quickly · Topic denial and PII redaction are configurable controls · No safeguard removes the need for evaluation
25 minHigh priority
Risk, Compliance and Enterprise Exposure
Classifying AI risk, meeting regulation and understanding where responsibility sits between you and the cloud provider.
- 3.9
Classifying AI Risk
Risk tier follows impact on people, money and reputation · Autonomy level and reversibility change the tier · Controls, review depth and monitoring scale with the tier · A risk register makes AI risk visible to the enterprise · Third-party AI inherits your risk classification
25 minHigh priority
- 3.10
The Regulatory Landscape and Standards
Risk-based regulation classifies systems by potential harm · ISO/IEC 42001 defines an AI management system · ISO/IEC 23053 frames ML system development · Sector rules often bite before AI-specific rules do · Compliance evidence has to be produced continuously
25 minHigh priority
- 3.11
Data Privacy, Access Control and Shared Responsibility
AWS secures the cloud; the customer secures what runs in it · Least privilege applies to model access and to data sources · RAG must respect the permissions of the underlying documents · Residency, retention and cross-border transfer are policy decisions · Prompts and outputs are records that may need retention rules
25 minHigh priority
- 3.12
Intellectual Property and Harmful Content Risk
Training data provenance drives copyright exposure · Ownership of generated output varies by jurisdiction · Feeding confidential material into a public tool can forfeit protection · Harmful, defamatory or unsafe output is a brand and legal risk · Indemnity terms differ sharply between vendors
25 minCore topic
Assess whether an organization is ready, close the gaps, lead people through the change and move AI out of pilot purgatory.
24% of the exam · Exam domain: Business Readiness, Leadership, and AI Transformation
Assessing AI Readiness
Maturity models, capability gaps and the data and platform foundations that decide what is possible.
- 4.1
AI Maturity Models and Where an Organization Sits
Maturity stages run from ad hoc experiments to embedded capability · AWS CAF covers business, people, governance, platform, security, operations · Assessment is evidence-based rather than aspirational · Maturity differs by business unit inside one company · The gap between stages sets the roadmap
25 minHigh priority
- 4.2
Capability Gaps Across People, Process, Technology and Governance
Four lenses keep an assessment from becoming a technology audit · A gap is only real when tied to a blocked use case · Some gaps are closed by hiring, others by partnering or buying · Sequence gap closure against the use case roadmap · Governance gaps stall delivery later, so they surface late
25 minHigh priority
- 4.3
Data Readiness, Silos and Ownership
Availability, quality, access and governance define data readiness · Silos are organizational before they are technical · Data ownership and stewardship need named roles · Sharing frameworks and contracts unblock cross-unit use · A data catalog turns hidden assets into usable ones
25 minHigh priority
- 4.4
Infrastructure and Platform Readiness
Managed services shorten the path from idea to pilot · Amazon Bedrock serves foundation model access without infrastructure · Amazon SageMaker AI serves custom model development · Amazon Quick brings AI assisted analytics to business users · Integration with existing systems decides whether value lands
22 minCore topic
Leading the Change
Sponsorship, champions, communication and the cultural resistance that decides adoption.
- 4.5
Executive Sponsorship and Funding Models
A sponsor supplies budget, air cover and decisions · Central, federated and hybrid funding shape behavior · Fund the capability, then fund use cases against it · Stage-gated funding limits exposure on unproven cases · Sponsor absence is the most reliable predictor of stall
25 minHigh priority
- 4.6
AI Champions and Cross-Functional Teams
Champions translate between the business and the technical team · Delivery needs domain, data, engineering and change skills together · Frontline involvement early raises adoption later · Communities of practice spread learning between teams · Champion time has to be protected and recognized
22 minCore topic
- 4.7
Communicating AI to the Workforce
Address the job security question directly and early · Explain what changes in the daily task, not the architecture · Silence gets filled with worse stories · Managers are the channel employees actually believe · Communicate limitations alongside capability to keep trust
25 minHigh priority
- 4.8
Cultural Barriers and Overcoming Resistance
Resistance is usually rational given local incentives · Fear, mistrust of output and loss of autonomy are the common roots · Visible wins from peers move more people than mandates · Incentives and performance measures must follow the new way of working · Pilot fatigue is a cultural barrier created by leadership
25 minCore topic
- 4.9
Building AI Literacy and Reskilling
Literacy differs by role: executive, manager, practitioner, general staff · Training tied to a real task beats generic awareness courses · Reskilling plans need a destination role, not just a course list · Safe sandboxes let people learn without risking data · Literacy reduces both shadow AI and unrealistic expectation
25 minHigh priority
Scaling From Pilot to Enterprise
Why pilots stall, what production actually demands and the operating model that keeps AI moving.
- 4.10
Why Pilots Stall
No production owner and no operating budget · Pilot data was hand-prepared and does not exist at scale · Integration and change work were never scoped · Success criteria were never agreed, so nobody can declare a win · Governance review arrives after the build, not before
25 minHigh priority
- 4.11
The Path From Pilot to Production
Define production readiness criteria before the pilot starts · Reliability, support, monitoring and rollback are production work · Phased rollout limits blast radius and builds evidence · Run cost and ownership transfer to a permanent team · Reusable platform components make the second use case cheaper
25 minHigh priority
- 4.12
AI Centers of Excellence and Operating Models
Centralized, federated and hub-and-spoke operating models · A center of excellence sets standards and reusable assets · Central control speeds governance and slows delivery · Federation speeds delivery and risks duplication · Most enterprises land on hub and spoke as they mature
25 minHigh priority
- 4.13
Sustaining Momentum and Managing the AI Portfolio
Treat AI initiatives as a portfolio with review cadence · Retire or fix systems that stop earning their run cost · Publish outcomes to keep sponsorship funded · Reuse and platform investment compound across use cases · A stop decision is a portfolio skill, not a failure
22 minCore topic
Each test mirrors the real exam: 130 minutes, 65 questions, all domains in proportion. Learning mode shows the explanation after each answer; exam mode runs the clock and scores at the end. The study plan below schedules them across the weeks.
- 1
Practice test 1
Full-length practice exam covering all four AIB-C01 domains at the published weights: AI fundamentals and literacy, AI strategy and business value creation, AI governance and responsible AI leadership, and business readiness, leadership, and AI transformation. Every question is a business scenario with a stated constraint.
65 questions · 130 min
- 2
Practice test 2
Second full-length practice exam at the published AIB-C01 domain weights. Covers AI solution selection and agent governance, build-buy-partner and platform migration decisions, pricing structures and cost control, risk tiering and incident response, bias monitoring over time, data sharing frameworks, and scaling with business continuity.
65 questions · 130 min
- 3
Practice test 3
Third full-length practice exam at the published AIB-C01 domain weights. Covers reading AI performance claims, labeling quality, multi-agent orchestration risk, embedded AI in existing software, research and development use cases, hidden review costs, automation bias, reclassifying systems that gain autonomy, model deprecation risk, and centre of excellence operating models.
65 questions · 130 min
- 4
Practice test 4
Fourth full-length practice exam at the published AIB-C01 domain weights. Covers foundation models and adaptation, agent tool permissions, prompt injection from untrusted documents, vendor exit terms, benefit attribution across simultaneous changes, total cost of ownership, proxy discrimination, AI system inventory, and the new roles AI adoption creates.
65 questions · 130 min
- 5
Practice test 5
Fifth full-length practice exam at the published AIB-C01 domain weights. Strongest coverage of the in-scope AWS surface: Amazon Bedrock, Amazon SageMaker AI, Amazon Quick, AWS CAF, Marketplace, and Pricing Calculator at a business level. Also covers rare-event training data, agent handoff, cost avoidance cases, human agency, log retention, and measuring value after a programme closes.
65 questions · 130 min
- 6
Practice test 6
Sixth full-length practice exam at the published AIB-C01 domain weights. Covers model versioning and auditability, timeliness against completeness in data, metric gaming, cost per outcome, concentration risk in AI providers, governing AI that arrives inside purchased software, incentive problems disguised as capability gaps, and the definition of done for an AI initiative.
65 questions · 130 min
Try 15 free questions on the certificate page before you buy.
How long does it take to prepare with this course?
About 21 hours of lessons, 52 of them at roughly 25 minutes each. The modules run in exam order, so the heaviest domains come first. Pick a pace and the plan lays itself out.
- Week 1AI Fundamentals and Literacy5h 8m of lessons5h 8m
- Week 2AI Strategy and Business Value CreationPractice tests 1 and 25h 50m of lessons5h 50m
- Week 3AI Governance and Responsible AI LeadershipPractice tests 3 and 45h of lessons5h
- Week 4Business Readiness, Leadership and AI TransformationPractice tests 5 and 6 · Schedule the exam once you clear 75% on a fresh test5h 16m of lessons5h 16m
Ready to start the AIB-C01 course?
The course and the practice tests come together in one purchase.
Course + practice tests
Lifetime access: pay once, study forever
- 52 interactive lessons with checkpoints
- 6 full-length practice tests
- 390+ exam-style questions
- Detailed explanations for every answer
- Exam mode & learning mode
- Unlimited retakes
- Access on any device
AIB-C01 course + practice tests
from$19.99course + tests