# AIB-C01 Course Overview The blueprint, weights, time allocation, hands-on preparation checklist and the mindset that the AWS Certified AI Business Strategist beta exam rewards. import { Card, CardGrid, Badge } from '@prosefly/astro-components'; AIB-C01 85 items · 170 min Pass 700/1000 $50 beta 4 domains · 13 tasks This is the full course for the **AWS Certified AI Business Strategist (AIB-C01)** beta exam: four domain deep dives, two 85-item mock exams and six long-form case studies. It is independent, unofficial preparation — see the [AWS preparation hub](/aws/) and [exam logistics](/aws/exam-logistics/) for the credential landscape, beta mechanics and scoring. This page is the map: what the credential validates, where the marks are, how to spend thirty hours, and the posture the exam consistently rewards. ## What the credential validates AIB-C01 validates **portable business judgment for AI decisions**, not AWS engineering. AWS states plainly that the exam *does not assess AWS services knowledge*; it assesses your ability to evaluate AI investments, build the business case, design governance and scale adoption across an organisation. The certified candidate is a product manager, program manager, consultant, business analyst, marketer or line-of-business leader who works alongside technical teams but builds nothing themselves — no coding, no data engineering, no tuning, no deployment, no security implementation. Where AWS services appear (Amazon Bedrock, Amazon SageMaker AI, Amazon Quick, AWS CAF, the shared responsibility model, pricing structures, the Pricing Calculator, Cost Explorer, Marketplace, the Well-Architected Responsible AI Lens), they appear only at the strategic level a leader needs to make a decision, never at console or API depth. AWS recommends about six months working with or alongside AI-adopting teams; there are no prerequisite certifications. ## Blueprint | # | Domain | Weight | Items (approx.) | Course page | | --- | --- | --- | --- | --- | | 1 | AI Fundamentals and Literacy | 24% | ~20 | [Domain 1](/aws/aib-c01/domains/d1-ai-fundamentals-and-literacy/) | | 2 | AI Strategy and Business Value Creation | **28%** | ~24 | [Domain 2](/aws/aib-c01/domains/d2-ai-strategy-and-business-value/) | | 3 | AI Governance and Responsible AI Leadership | 24% | ~21 | [Domain 3](/aws/aib-c01/domains/d3-governance-and-responsible-ai/) | | 4 | Business Readiness, Leadership, and AI Transformation | 24% | ~20 | [Domain 4](/aws/aib-c01/domains/d4-readiness-leadership-transformation/) | Four domains, 13 task statements, nothing below 24%. This is the flattest blueprint on the site: there is no domain you can safely skip, and a single weak domain is roughly a quarter of the exam. :::tip[Where the marks are] Domain 2 — **AI Strategy and Business Value Creation, 28%** — is the single heaviest block and the one that most rewards preparation, because its items turn on arithmetic you can practise: ROI, payback, baselines, KPI design and the scale/pause/terminate call. The other three domains sit at 24% each. Master the Domain 2 numbers first; they also underwrite the value arguments that Domains 3 and 4 assume. ::: :::note[Two independent mock exams] This course ships **two** full-length, blueprint-weighted mock exams of 85 items each. [Mock Exam 1](/aws/aib-c01/practice-exam/) is your **diagnostic** — sit it first, untimed, to find your two weakest domains. [Mock Exam 2](/aws/aib-c01/practice-exam-2/) is the **timed readiness gate** — sit it at 170 minutes and treat 75% raw as the floor before you book. Both report a raw percentage plus an indicative scaled figure against the 700 line, clearly labelled as a rough guide, because AWS does not publish the raw-to-scaled mapping. Neither mock contains official AWS questions. ::: ## The mindset this exam rewards The exam is written for someone who has watched AI pilots succeed and fail and learned which instincts pay off. Correct answers consistently: - **Define the outcome before buying technology.** A tool is not a strategy; a use case with a measurable outcome is. - **Establish a baseline before deployment.** Value you cannot measure is value you cannot defend, renew or scale. - **Kill or pause initiatives** that fail on value, feasibility, data readiness, sustainability or strategic alignment, rather than pushing them into production because sunk cost feels like momentum. - Choose **rule-based automation** when the problem is deterministic, and say so without embarrassment. - Treat **governance as design input**, not a legal review bolted on the day before launch. - Insist on **human oversight** where a decision is consequential, irreversible, regulated or touches personal data. - Fix **data and accountability foundations before scaling**, because scale multiplies whatever you already have — including the flaws. - Prefer **short-term wins that build toward a reusable enterprise pattern** over a big-bang programme. Wrong answers buy technology before defining the outcome, measure activity instead of value, scale a pilot whose success was never measured, treat responsible AI as an end-stage sign-off, and answer an organisational problem with a technical purchase. ## Suggested time allocation A **30-hour** plan, weighted to the blueprint with a little extra on Domain 2 because its arithmetic is the highest-leverage practice on the exam. | Activity | Hours | | --- | --- | | D2 · AI Strategy and Business Value Creation (28%) | 8 | | D1 · AI Fundamentals and Literacy (24%) | 6 | | D3 · AI Governance and Responsible AI Leadership (24%) | 6 | | D4 · Business Readiness, Leadership, and AI Transformation (24%) | 6 | | Case studies and cross-domain reasoning | 2 | | Mock Exam 1 (diagnostic) plus review | 1 | | Mock Exam 2 (timed) plus review | 1 | | **Total** | **30** | ## Hands-on preparation checklist These are exercises a business professional can actually do without building anything. Doing them once beats re-reading a page three times, because every one produces an artefact the exam expects you to reason about. - [ ] Draft a **one-page business case** for a real AI use case in your organisation, with a measurable baseline, a target, an estimated cost and a payback figure. - [ ] Build a **KPI tree** for one use case: one north-star outcome KPI at the top, the leading indicators that predict it below, and the guardrail metrics that must not degrade. - [ ] Write an **AI tool classification list** for your team — approved, blocked and under evaluation — and name the criterion that moves a tool between columns. - [ ] Run a **build-buy-partner comparison** for one capability, using AWS Marketplace to scope buy/partner options and the AWS Pricing Calculator to forecast a consumption-based cost against a build estimate. - [ ] Draft a **governance RACI** for one AI initiative — who is responsible, accountable, consulted and informed for the go/no-go, the data decision and the human-review gate. - [ ] Classify **three use cases by risk tier** (minimal / limited / high, in the spirit of a risk-tiered framework) and write the control that each tier triggers. - [ ] Write a **90-day pilot-to-scale plan** with a short-term win, a success metric, a go/no-go gate and the conditions under which you would scale, pause or terminate. - [ ] **Interview a technical colleague** about data readiness for one use case: quality, accessibility, ownership and silos — and write down what you learned that you could not have guessed. - [ ] Estimate a use case's cost under **consumption, instance and seat-based** pricing and note which is cheapest at low volume and which at high volume. - [ ] Sit [Mock Exam 1](/aws/aib-c01/practice-exam/) as a diagnostic and list your two weakest domains before revising. ## Course pages 24% · ~20 items 28% · ~24 items 24% · ~21 items 24% · ~20 items Six worked organisational scenarios 85 items · diagnostic 85 items · timed readiness gate Beta mechanics and scoring CAF, responsible AI, ISO, NIST Bedrock, SageMaker AI, Quick at a strategic level Pricing structures with worked maths Tangible and intangible metrics by function