AWS Certified AI Business Strategist (AIB-C01) Exam Guide (2026)

If you can decide whether a chatbot stays a pilot, how you would prove ROI after 90 days, and who owns the risk when a model invents a refund policy, you are in the right exam.

AIB-C01 is AWS's business-category AI certification. It is not AIF-C01. AWS says this exam tests the judgment that takes AI from an experiment to a funded, governed program. It does not test whether you can configure Bedrock.

This guide is for people sitting the current AIB-C01 outline. The exam is in beta. AWS offers an extra Early Adopter digital badge if you earn the certification by 15 February 2027.

Who this exam is for

Take it if you are a product manager, program manager, sales or business development person, line-of-business lead, consultant, marketer, or business analyst who evaluates, funds, or scales AI work. You work next to the builders. You do not build the models.

Skip it if you want items that ask Bedrock versus SageMaker versus Comprehend. That is AIF-C01. The AWS Certified AI Practitioner page states the split in those words. AIB validates which investments to pursue, how to build the business case, how to apply governance, and how to move a pilot into production. AIB does not assess knowledge of AWS AI services. AIF validates AI, ML, and generative AI concepts and AWS AI services.

AWS does not require coding, hands-on AWS implementation, or another AWS certification. AWS recommends about 6 months working with or alongside AI initiatives. You should recognize recommendation engines, NLP, computer vision, document extraction, and AI in customer operations as business tools, not as services you deploy.

The official target candidate works for the organization or for a client. Sales people who must explain AI value, consultants who take a client from a demo to a funded program, and program managers who align spend to an outcome all sit this exam. You need enough literacy to question a technical team. You do not need to ship the model.

If the daily work is IAM JSON, Glue jobs, or SageMaker training loops, sit a builder exam instead. AIB lists those jobs as out of scope.

Exam shape

AWS currently weights four domains:

Domain 2 is where people fail if they only memorized AI terms. The items look like funding and stop-or-scale decisions.

Read the stem for the constraint before you pick a logo. If the item names a service and asks you to configure it, you are on the AIF path. If the item names a budget, an owner, a risk class, or a 90-day metric, you are on the AIB path.

The exam uses multiple choice and multiple response. One correct answer of four. Or two or more correct answers of five or more. You must pick every correct option to score the item. Unanswered items score as wrong. There is no extra penalty for a guess.

Pass mark is a scaled 700 on a 100 to 1,000 scale. Scoring is compensatory. You pass the exam as a whole, not each domain. The score report may show a section table. Treat that table as a hint about weak spots. It is not a second pass mark.

AWS expects strategic familiarity with Amazon Bedrock, Amazon SageMaker AI, and Amazon Quick. Know Bedrock as a generative AI platform with pricing tiers, Guardrails, and Knowledge Bases. Know SageMaker AI as the custom ML path and when a managed service is enough. Know Amazon Quick as an AI-powered business assistant. Pair those names with AWS CAF, the shared responsibility model for AI workloads, and the Well-Architected Responsible AI Lens. That is naming and fit, not console work.

In-scope pricing language stays at the business-case level. Consumption, instance, and seat pricing. Savings Plans as a cost control. AWS Pricing Calculator and AWS Cost Explorer for a plan. AWS Marketplace when the stem is build, buy, or partner.

Early adopter window

AWS is running AIB-C01 as a beta. The AWS Certified AI Business Strategist page lists 85 questions, 170 minutes, English or Japanese, Pearson VUE or online proctoring, and $50 beta pricing against a $100 standard price.

The exam guide lists 130 minutes as the exam duration. Use the booking page for seat time and price. Use the exam guide for domains and tasks.

The AWS Training and Certification launch post says beta registration opened on 1 September 2026 and exam delivery begins on 29 September 2026. Earn the certification by 15 February 2027 and AWS adds an Early Adopter digital badge on top of the credential badge.

A beta sitting is still a real certification. AWS uses beta exams to validate items before the standard form. Candidates who pass hold the certification. Official practice exams are listed as not available during beta. Official practice question sets and walk-through questions on Skill Builder are the practice AWS points to now.

If you already hold AIF-C01, AWS treats AIB as a complement, not a replacement. The next step after AIB, if you want deeper AWS AI technology, is still AIF-C01, then Machine Learning Engineer Associate or Generative AI Developer Professional. If you want a broader cloud base, Cloud Practitioner stays the named option.

How funding choices fail

Domain 2 is 28 percent of scored content. The stem will give you a vendor number, a timeline, and a promised outcome. The work is to decide whether the spend is sound, early, or missing a success factor.

Start with the baseline. AWS asks you to set metrics before the system goes live so you can measure the effect of adoption. A pitch that cannot name the current handle time, error rate, or conversion rate has no ROI story. Write the KPI that would kill the project. Tangible numbers include cost reduction and revenue growth. Intangible numbers include customer satisfaction and employee productivity. Leading indicators matter when the lagging ROI will take a year.

Build, buy, or partner is a constraint question. Budget, timeline, in-house skill, the vendor proposal, and regulation all sit in the stem. A cheap model that your team cannot operate is still a bad buy. A custom SageMaker path that a partner could deliver faster can still be the wrong spend if the process is not ready.

Skill 2.1.3 is scale, pause, or terminate. Skill 2.1.4 is when AI is not the appropriate solution. A deterministic refund table, a stable rules engine, or a process with no labeled data is often a rule, not a model. Transition stories add business continuity, cost, data readiness, and performance. Moving a process onto AI, or from one AI platform to another, is a program decision.

Competitive items ask whether the industry is already using AI and what investment level matches that maturity. A late mover that funds a chatbot with no data owner is not creating a durable advantage. A firm that changes the business model, not only the tool, is closer to the Domain 2 answer.

How to study without wasting a month

Week 1. Literacy that shows up in stems. AI, ML, and GenAI as different bets. Structured versus unstructured data, and why junk labels wreck outcomes. When a rule or a purpose-built process beats a model. What an agent can do that a chatbot cannot. Autonomy, tool use, agent-to-agent communication, and orchestration are the agent words AWS lists. Prompt, RAG, and fine-tune as ways to change an output, not as homework in SageMaker. Token limits and context windows show up as reasons a knowledge task fails, not as API knobs. Classify tools as approved, blocked, or under evaluation so shadow AI has an owner. ISO/IEC 42001 and ISO/IEC 23053 show up as shared vocabulary, not as a law exam.

Week 2. The Domain 2 business case. For each story, name the outcome, the baseline you would measure first, and the KPI that would kill the project. Practice build, buy, or partner with budget, time, skill, and regulation in the stem. Write one sentence for when AI is the wrong spend. Know consumption, instance, and seat pricing only at the level of a business case. Map one use case each to customer operations, sales and marketing, research, and software development so Domain 2.1 is not a blank.

Week 3. Governance before the launch date. Fairness, explainability, privacy, safety, transparency. The exam asks what you give up when a metric fights a principle. Name who is accountable and put cross-functional seats on the governance group. Put a risk class on the system and carry that class through the lifecycle. Decide when a human must see the output. Name hallucination detection, guardrails, and escalation criteria as safeguards, not as model trivia. Treat bias drift, harmful content, IP leakage, and data quality drop as program risks you monitor in production.

Week 4. Readiness and scale. Score the org on leadership alignment, data quality, culture, infrastructure, and governance. Find the silo that blocks the use case. Name data ownership and a sharing rule before you fund a company-wide rollout. Plan a short win, then a scale path. AWS describes an iterative path of envision, experiment, launch, and scale. Know what an AI center of excellence is for. Build literacy with proof of concept programs, hackathons, training, and responsible AI training, which are the workforce methods AWS lists. If the stem is still a proof of concept with no owner and no metric, the answer is not "roll it out company-wide."

Stay inside the official out-of-scope fence. Do not spend the month writing IAM policies, wiring Lambda, sizing EC2, or tuning hyperparameters. The out-of-scope service list includes compute, networking, databases, storage, containers, developer tools, and security configuration. The exam asks you to understand AI services at a strategic level and to make a business decision about their use.

Traps if you studied AIF-C01

AIF-C01 and AIB-C01 share words. They do not share the right answer.

• Sentiment on support tickets is often a service pick on AIF. On AIB it is a value, risk, and scale question.
• "Answer from our 10,000 PDFs" on AIF is RAG. On AIB it is whether retrieval is enough to hit the KPI, who owns the corpus, and what happens when the documents are wrong.
• A chatbot that must not invent refund policy is a guardrail item on AIF. On AIB it is human oversight, risk class, and whether the process is ready.
• Memorizing IAM JSON, hyperparameter knobs, or pipeline steps is out of scope. AWS lists those as tasks the candidate is not expected to do.
• If you can delete the business constraint and still pick the answer from a service name, you are practicing the wrong exam.
• Shadow AI is a classification and ownership problem. Approved, blocked, or under evaluation. It is not an IAM deny statement.
• An agent item is about autonomy and tool use, not about which SDK starts the agent.
• A fairness metric that fights a revenue KPI is a tradeoff you must name. It is not a reason to skip governance so the launch date holds.
• A maturity item that still says "experiment" is not a company-wide scale answer. Envision, experiment, launch, then scale.
• Bedrock Guardrails or a Knowledge Base on this exam is a control or a retrieval choice in a business case. It is not a console lab.

The official out-of-scope job list is the fastest filter for practice items. If the task is code a model, clean a dataset by hand, pick an algorithm, deploy a pipeline, or administer an AWS account, drop the item. That is a different certification.

How this maps to CloudFluently

Study with the AWS AI Business Strategist practice exam sets. AWS AI Practitioner practice sets train AIF-C01 habits. Use AWS's exam guide for AIB task statements.

Official outline: AWS Certified AI Business Strategist (AIB-C01).

Frequently Asked Questions

Is AIB-C01 the same exam as AIF-C01? No. AIB is the business-category exam for funding, governance, and scale. AIF is the practitioner exam for AI concepts and AWS AI services. AWS says you can earn both.

Does this exam require coding or another AWS certification? No. AWS requires neither coding nor hands-on AWS implementation. AWS recommends about 6 months working with or alongside AI initiatives.

How long is the sitting and how many questions are there? The booking page lists 85 questions and 170 minutes for the beta. The exam guide lists 130 minutes. Book from the certification page. Study domains from the exam guide.

What is the pass mark? A scaled 700 on a 100 to 1,000 scale. Scoring is compensatory. You pass the exam as a whole.

What should you study first? Domain 2 business cases with a baseline and a kill metric, then Domain 3 accountability and risk class. Domain 2 is 28 percent.

When does the Early Adopter badge end? Earn the certification by 15 February 2027. AWS then adds the Early Adopter digital badge.

Where is the official outline? AWS Certified AI Business Strategist (AIB-C01). Booking and beta price: AWS Certified AI Business Strategist. Use AWS for task statements and weights. Use this page for who should sit it and how it differs from AIF-C01.