AWS Certification Preparation

6 credentials 2 foundational · 3 associate · 1 business 3 practice exams 229 glossary entries

Independent, unofficial preparation for the AWS certification path. Five service-based credentials — Cloud Practitioner, Solutions Architect – Associate, CloudOps Engineer – Associate, Developer – Associate and AI Practitioner — plus the business-category AI Business Strategist beta, which is unlike all of them.

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The six credentials

Certification	Code	Level	Items	Time	Pass	Price
Cloud Practitioner	CLF-C02	Foundational	65	90 min	700/1000	$100
AI Practitioner	AIF-C01	Foundational	65	90 min	700/1000	$100
Solutions Architect – Associate	SAA-C03	Associate	65	130 min	720/1000	$150
CloudOps Engineer – Associate	SOA-C03	Associate	65	130 min	720/1000	$150
Developer – Associate	DVA-C02	Associate	65	130 min	720/1000	$150
AI Business Strategist	AIB-C01	Business (beta)	85	170 min	700/1000	$50

The certification tracks page breaks down every blueprint, the exam versions currently in transition, and which credential to sit first for your role.

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AWS Certified AI Business Strategist (AIB-C01)

AIB-C01 85 items · 170 min Pass 700/1000 $50 beta

Validate the business judgment that takes AI from adoption to scale.

The AWS Certified AI Business Strategist (AIB-C01) is AWS’s business-category credential for people who evaluate, champion and scale AI initiatives. It tests whether you can pick the right AI investments, build the business case, design governance, and move an organisation from experimentation to measurable outcomes. AWS is explicit about one thing that shapes every page of that course: the exam does not assess AWS services knowledge. It assesses strategic decision-making that is portable across organisations and industries.

What the credential validates

AWS’s stated task list for a certified AI Business Strategist:

• Apply foundational AI concepts and terminology to business contexts.
• Select appropriate AI solution types based on business requirements and constraints.
• Use GenAI techniques, including prompt engineering and model adaptation, to achieve business outcomes.
• Develop and prioritise AI strategies that align with organisational objectives.
• Measure AI business value using KPIs, ROI frameworks and baseline metrics.
• Position AI initiatives for competitive advantage and business-model transformation.
• Apply responsible AI principles when making decisions that require trade-offs.
• Establish governance structures and ensure regulatory compliance.
• Identify enterprise AI risks and direct appropriate mitigation strategies.
• Assess organisational AI readiness and maturity across people, process, 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 from pilots to enterprise-wide deployments using iterative approaches.

The blueprint

#	Domain	Weight	Items (approx.)	Course page
1	AI Fundamentals and Literacy	24%	~20	Domain 1
2	AI Strategy and Business Value Creation	28%	~24	Domain 2
3	AI Governance and Responsible AI Leadership	24%	~21	Domain 3
4	Business Readiness, Leadership, and AI Transformation	24%	~20	Domain 4

Four domains, 13 task statements, and no domain below 24% — this is the flattest blueprint of any exam on this site. There is no domain you can safely skip.

AIB-C01 versus AWS Certified AI Practitioner

The two are built for different purposes and it is worth being precise, because candidates routinely prepare for the wrong one.

	AI Business Strategist (AIB-C01)	AI Practitioner (AIF-C01)
Validates	Business judgment: which investments, what business case, which governance, how to scale	Foundational knowledge of AI, ML and GenAI concepts and AWS AI services
Assesses AWS services?	No – strategic familiarity only	Yes
Category	Business	Foundational
Typical candidate	Product and program managers, sales and business development, line-of-business leaders, consultants, business analysts, marketers	Anyone needing foundational AI fluency on AWS, including technical staff
Coding required	None	None, but more technical vocabulary

You can hold both. AWS’s suggested progression after AIB-C01: AI Practitioner, then Machine Learning Engineer – Associate or Generative AI Developer – Professional if you want depth in AI/ML services, or Cloud Practitioner for a broader cloud foundation.

How the AIB-C01 course is built

Every domain page carries the same structure used across this site: a framing paragraph, learning objectives mapped to the official skills, numbered concept sections built one-to-one on the task statements, a named decision framework, a common-mistakes table, a long scenario walkthrough with an expert reasoning trace, an exam-traps table, 20–24 practice questions with full distractor analysis, and key takeaways.

Two full-length mock exams follow the real shape: 85 items, 170 minutes, weighted to the blueprint, scored as a raw percentage with an indicative scaled figure against the 700 pass mark.

The AWS appendix carries the reference material you should not have to re-derive: AWS CAF, the responsible AI dimensions and the Well-Architected Responsible AI Lens, the shared responsibility model, ISO/IEC 42001 and 23053, the NIST AI RMF, the service landscape at a business level, pricing structures with worked ROI maths, a governance toolkit and a KPI library.

The mindset the exam rewards

The exam is written for someone who has watched AI pilots succeed and fail. Correct answers tend to:

• Establish a baseline before deployment, because value you cannot measure is value you cannot defend.
• Kill or pause initiatives that fail on feasibility, data readiness or strategic alignment, rather than pushing them into production.
• Choose rule-based automation when the problem is deterministic, and say so plainly.
• Treat governance as design input, not as a gate bolted on before launch.
• Insist on human oversight where decisions are consequential, irreversible or regulated.
• Fix the data and accountability foundations before scaling, because scale multiplies whatever you already have.
• Prefer short-term wins that build toward an enterprise pattern over a big-bang programme.

Wrong answers tend to: buy technology before defining the outcome, measure activity instead of value, scale a pilot whose success was never measured, treat responsible AI as a legal review at the end, and answer an organisational problem with a technical purchase.