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Technologies and concepts that might appear on the exam - AWS Certified AI Business Strategist

Technologies and concepts that might appear on the exam

The following list contains technologies and concepts that might appear on the exam. This list is non-exhaustive and is subject to change. The order and placement of the items in this list is no indication of their relative weight or importance on the exam:

  • Core differences between AI, machine learning (ML), and generative AI (GenAI)

  • How AI models are trained, make predictions, and improve over time

  • Types of data (for example, structured, unstructured) and why data quality matters for AI

  • Basics of prompt engineering and how to get useful AI outputs

  • How context windows and tokens affect GenAI performance

  • Techniques for improving AI responses (for example, Retrieval Augmented Generation [RAG], fine-tuning)

  • What AI agents are and how they differ from other AI tools

  • When to use rule-based automation instead of AI

  • Why AI systems need ongoing monitoring (for example, model drift, performance changes)

  • Risks of unapproved AI tool usage (shadow AI) and how to manage risk

  • Measuring AI value by using key performance indicators (KPIs), return on investment (ROI), cost savings, and productivity gains

  • Build-buy-partner decisions for AI solutions

  • Prioritizing AI initiatives by business impact and feasibility

  • Responsible AI principles (for example, fairness, explainability, privacy, safety, transparency)

  • Governance structures and who is accountable for AI decisions

  • Risk classification for AI systems

  • Regulatory and compliance considerations for AI

  • Human oversight, guardrails, and knowing when to escalate

  • Common AI reliability issues (for example, hallucinations, bias, data quality degradation)

  • Intellectual property (IP) considerations when using AI

  • Assessing organizational AI readiness and maturity

  • Data strategy, data ownership, and breaking down data silos

  • Leading change management and workforce transformation for AI

  • Building AI literacy across an organization

  • Scaling AI from pilots to enterprise-wide adoption

  • AI centers of excellence (COEs)

  • Amazon Bedrock, Amazon SageMaker AI, and Amazon Quick (at a strategic level)

  • AWS Cloud Adoption Framework (AWS CAF)

  • AWS shared responsibility model for AI workloads

  • AWS AI pricing and cost optimization strategies

  • Global AI standards and frameworks (for example, ISO/IEC 23053, ISO/IEC 42001)