Result I took the AB1-C01 beta on the day it opened and passed with 743 (700 needed). It's...

## Result I took the AB1-C01 beta on the day it opened and passed with 743 (700 needed). It's my 15th AWS certification and came with the Early Adopter badge. Item | Detail ---|--- Exam | Beta - AWS Certified AI Business Strategist (AB1-C01) Exam date | 29 Sep 2026 (beta launch day) Score | 743 / 1000 (pass mark 700) Length | 85 questions, 3 hours My prep | Stephane Maarek's course, slides and practice questions, plus the Skill Builder official practice set. Nothing else. Valid until | 29 Sep 2029 ## Exam format Four domains, weighted close to evenly, so you can't skip any of them. Scoring is compensatory: you pass on the total score, not on each domain. Domain | Weight | How it felt ---|---|--- 1. AI Fundamentals and Literacy | 24% | Easy if you've worked with AI before 2. AI Strategy and Business Value Creation | 28% | Heavy on business vocabulary 3. AI Governance and Responsible AI Leadership | 24% | Lots of terms to memorise 4. Business Readiness Leadership | 24% | Lots of terms, plus the adoption journey You get about 2 minutes per question. My rough guess is that you need around 60 of 85 correct, but the score is scaled, so treat that as a feel, not a rule. ## Study resources I used Stephane Maarek's course and the Skill Builder practice set, and nothing else. His review section and slides helped most, because this exam is mostly about getting the vocabulary to stick. Resource | Time | Did I use it? ---|---|--- Stephane Maarek's course (Udemy) | About 3.5 hrs | Yes, as my main resource. Watch it once, then rewatch the review section. Stephane Maarek's slides | Revision | Yes. Reread them the day before, because this is where the terms stick. Stephane Maarek's practice tests | Varies | Yes, about 30 questions. Do more if you can. Skill Builder official practice question set | 20 questions | Yes. Use it to calibrate yourself before the exam. AWS Skill Builder: AI Business Strategist learning plan | About 12–13 hrs | No. It's optional and only worth it if you're new to AI. ## Domain by domain I put most of my focus on Domains 1 and 4. Domains 2 and 3 are where the vocabulary piles up. **Domain 1: AI Fundamentals and Literacy** * How AI, ML and generative AI differ * Prompt engineering basics * When AI is the right choice, and when it isn't * Fine-tuning vs RAG, and what each is actually for * Structured vs unstructured data, and pre-trained vs fine-tuned models **Domain 2: AI Strategy and Business Value Creation** * Build, buy or partner, and when to pick each one * Tying an AI use case to a measurable business outcome * The adoption journey: Envision, Experiment, Launch, Scale **Domain 3: AI Governance and Responsible AI Leadership** * Responsible AI ideas such as fairness, transparency and explainability * Data drift vs model or performance drift * Governance: who owns the model, the data and the risk * SageMaker Clarify for bias and explainability, and SageMaker Model Monitor for watching models in production **Domain 4: Business Readiness Leadership** * Deciding whether an organisation is ready to adopt AI * Scaling without building from scratch, for example with Amazon Bedrock * Knowing broadly what Amazon SageMaker covers when you do need to build ## Vocabulary cheat sheet Most of this exam is vocabulary. Know each of these in one line. Term | What it means ---|--- AI / ML / GenAI | AI is the broad field. ML is a subset that learns patterns from data instead of following rules. GenAI is a subset of ML that creates new content. Algorithm | The method or recipe used to learn from data Training | Running the algorithm on historical data Model | The result of training: the learned patterns. You can't write one by hand. Inference | Using the trained model on new data Prediction | The output of inference, such as a score, a label or some text Structured vs unstructured data | Rows and columns vs free text, images, audio and PDFs Foundation model | A large pre-trained model you can use as-is or adapt Token | A piece of a word. The unit a GenAI model counts for both capacity and cost. Context window | The most tokens one exchange can hold, input and output together Prompt engineering | Better output from better instructions, with no change to the model RAG | Handing the model your documents at query time so answers use current facts Fine-tuning | Retraining a copy of the model on your examples. It changes the weights and costs the most. Hallucination | A confident answer that is wrong or made up AI agent | A system that takes actions on its own, not just answers Data drift | Live inputs no longer look like the training data Model quality drift | Predictions get less accurate over time Bias drift | Accuracy holds overall but gets worse for one group Feature attribution drift | The inputs the model leans on have changed Baseline | The before-state, measured before launch the same way you'll measure after Transparency vs explainability | People know AI is involved, vs someone can give the reason for one specific outcome Accuracy vs fairness | Accuracy is measured across everyone. Fairness is measured group by group. Shadow AI | AI tools staff use without review or approval Center of Excellence (CoE) | A central team that shares AI patterns, standards and advice. It should enable teams, not become a bottleneck. ISO/IEC 42001 | The certifiable AI management system standard, for governing AI across an organisation ISO/IEC 23053 | A framework standard that defines common vocabulary for ML-based AI systems Amazon Bedrock | Managed access to foundation models through one API, with nothing to build or host Amazon Bedrock Guardrails | Filters harmful content and masks sensitive data in prompts and answers Amazon SageMaker AI | The platform for building, training and deploying your own models SageMaker Clarify | Detects bias and explains predictions SageMaker Model Monitor | Compares live data and predictions against a baseline and alerts on drift ## Exam traps and decision rules These are the rules I'd revise the night before. **The ML chain, in order** * Data → algorithm → training → model → inference → prediction. Know this order. * The algorithm is the recipe, and the model is the result. Inference is the action, and the prediction is its output. * There's no model without training, and no inference before a model exists. * Training cost is paid at launch and again each time you retrain. Inference cost is paid on every use, so it grows with usage. **Drift: when the model sees new data** * A model can be right at launch and wrong six months later. Nothing crashes and no error is raised. * Watch four kinds of drift: data, model quality, bias and feature attribution. * Capture a baseline at training time. Compare live inputs and outputs to it on a schedule, then alert and retrain or fix the data source when a threshold is crossed. * Some problems look like drift but aren't: a capacity limit, a bad prompt, or normal day-to-day variation. **Tokens and the context window** * Tokens decide what fits and what you pay. * Everything counts against the context window: the question, chat history, retrieved documents, examples and the answer. * A short question can reach the model as thousands of tokens. **Prompt, RAG or fine-tune** * Always start with the prompt, because it's the cheapest. * If the model needs new knowledge, use RAG. Stale facts are a retrieval problem. * If it has the right facts but the wrong tone or format, fine-tune. **Which kind of AI, if any** * An assistant gives an answer, a dashboard gives a view, a predictive model gives a score, and an agent takes an action. * Only an agent acts on its own. Choose one only when the task truly needs autonomous action. * Some things that sound like AI only need rules. Rules are cheaper and don't drift. **Build, buy or partner** * Build when your advantage lies in your own data or logic. * Buy when you need speed and the capability doesn't set you apart. * Partner when you lack expertise you can't hire in time. * The answer depends on each use case, not on a company-wide policy. **Envision → Experiment → Launch → Scale** * Envision: exploring opportunities, with nothing in production. * Experiment: controlled pilots, where success means learning. * Launch: proven pilots in production, with value measured. * Scale: AI across functions, with governance and continuous improvement. * Some AWS material calls stage two "Align." The exam uses "Experiment." * Readiness is set by your weakest dimension, not by an average. **Prove the value** * Record a baseline before launch. Without one, an improvement is a claim, not evidence. * Attribution means showing the AI caused the change, not just that the number moved. * Every initiative ends in one of three decisions: scale, pause with a fix and a date, or terminate. * Money already spent is never a reason to continue. **Governance and risk** * Risk class = severity of a wrong outcome × likelihood. Regulated uses never sit below high. * Reassess the risk when the use changes, for example when an internal tool starts facing customers. * Confidence-based routing lets the system decide when it's confident and sends the rest to a person. * ISO 27001 and SOC 2 are security certifications, not AI governance. A vendor's "Responsible AI" page is marketing, not proof. * When you find shadow AI, give people an approved path first. Punishment doesn't fix it. **AWS CAF, ROI and cost tools** * **AWS Cloud Adoption Framework (CAF):** AWS guidance for assessing readiness across six perspectives: Business, People, Governance, Platform, Security and Operations. Its phases are Envision, Align, Launch and Scale. * **ROI:** (value gained − total cost) ÷ total cost. Count the running costs, such as inference, monitoring and people, not just the build. * **AWS Pricing Calculator:** estimates the cost of a proposed solution before you build it. Use it for the business case. * **AWS Cost Explorer:** shows what you are actually spending. Tag each AI initiative so its cost can be tracked on its own. * **Savings Plans:** a 1- or 3-year spend commitment in exchange for lower prices. They suit steady, predictable workloads, not early pilots. ## Exam-day strategy: eliminate options Option elimination did more for me than anything else. On most questions I could rule out two answers straight away. 1. Read the question for the business goal first: cost, speed, risk, or value. 2. Cross out the two options that clearly don't fit that goal. 3. Between the last two, pick the one that solves the business need with the least effort or risk. 4. Flag it and move on. At 85 questions in 3 hours, you have about 2 minutes per question. 5. Save energy for the last hour. That's when every option starts to look right. ## My recommended path If you already work with AI, Domain 1 will feel easy, and your time should go to the business and governance terms. If you're from a non-tech background, start with the ML chain and the vocabulary table. * [ ] Watch Stephane Maarek's course (about 3.5 hrs) * [ ] Rewatch the review section and reread the slides until the vocabulary sticks * [ ] Do Stephane Maarek's practice questions * [ ] Take the 20-question official practice set on Skill Builder * [ ] Optional: the Skill Builder learning plan (12–13 hrs), only if you're new to AI * [ ] On exam day, eliminate options and keep a pace of about 2 minutes per question The certificate is the start, not the finish. I'll be going back through the course to turn this into better business conversations with clients. _Written by Vishnu Rachapudi. More guides and hands-on AWS content at vishnurachapudi.com._

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