AWS Certified AI Practitioner Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Fundamentals of AI and ML (20% of the exam)

Task Statement 1.1: Explain basic AI concepts and terminologies.

• Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM]).
• Describe the similarities and differences between AI, ML, and deep learning.
• Describe various types of inferencing (for example, batch, real-time).
• Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
• Describe supervised learning, unsupervised learning, and reinforcement learning.

1.1 summary: This section makes sure you understand the foundation of AI and how it connects to ML and deep learning. You will explore different learning techniques, the types of data used in AI models, and the essentials of training versus inferencing. The goal is to develop a confident grasp of key terminology so you can participate in AI-focused conversations with clarity.

You will also learn how these core ideas apply to real-world use cases. Whether it is text, images, or time-series datasets, understanding how different data types shape AI outcomes positions you to recognize opportunities and think strategically about how AI systems are designed.

Task Statement 1.2: Identify practical use cases for AI.

• Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).
• Determine when AI/ML solutions are not appropriate (for example, cost benefit analyses, situations when a specific outcome is needed instead of a prediction).
• Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).
• Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).
• Explain the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

1.2 summary: In this section you will match common business problems with suitable AI or ML techniques. You will practice differentiating between solutions that support automation, decision enhancement, or entirely new capabilities, and learn when AI may not be the right approach. This builds your judgment as you balance technical possibilities with business priorities.

You will also recognize real-world examples of AI in action and connect them to AWS-managed services. This gives you the ability to explain both the value and the practical application of AI to colleagues and decision makers, making you an asset in strategic discussions.

Task Statement 1.3: Describe the ML development lifecycle.

• Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).
• Understand sources of ML models (for example, open source pre-trained models, training custom models).
• Describe methods to use a model in production (for example, managed API service, self-hosted API).
• Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Model Monitor).
• Understand fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).
• Understand model performance metrics (for example, accuracy, Area Under the ROC Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.

1.3 summary: This section walks you through the major phases of an ML project, from data preparation to model monitoring. You will connect lifecycle steps to AWS tools that simplify each stage, giving you insight into how complete ML solutions are built.

You will also learn how to evaluate models from both a technical and business perspective. This ensures that you can articulate not only whether a model performs well statistically, but also whether it delivers meaningful value and supports broader business objectives.

Domain 2: Fundamentals of Generative AI (24% of the exam)

Task Statement 2.1: Explain the basic concepts of generative AI.

• Understand foundational generative AI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, diffusion models).
• Identify potential use cases for generative AI models (for example, image, video, and audio generation; summarization; chatbots; translation; code generation; customer service agents; search; recommendation engines).
• Describe the foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).

2.1 summary: This section highlights the fundamental mechanics of generative AI. You will become familiar with terminology like tokens, embeddings, and transformers, and understand how these elements combine within foundation models to power generative applications.

Equally important, you will connect these concepts to potential business use cases, from chatbots to creative tools. You will also explore the foundation model lifecycle, enabling you to appreciate how these powerful systems evolve from pre-training through deployment and beyond.

Task Statement 2.2: Understand the capabilities and limitations of generative AI for solving business problems.

• Describe the advantages of generative AI (for example, adaptability, responsiveness, simplicity).
• Identify disadvantages of generative AI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism).
• Understand various factors to select appropriate generative AI models (for example, model types, performance requirements, capabilities, constraints, compliance).
• Determine business value and metrics for generative AI applications (for example, cross-domain performance, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).

2.2 summary: This section emphasizes evaluating the strengths of generative AI while being mindful of its limitations. You will learn how to match model capabilities with real-world requirements, keeping critical factors such as compliance, performance, and accuracy in mind.

You will also explore business value metrics that help determine the effectiveness of generative AI implementations. This adds a strategic layer to your knowledge, enabling you to evaluate not just the technical fit of a solution but its overall business impact.

Task Statement 2.3: Describe AWS infrastructure and technologies for building generative AI applications.

• Identify AWS services and features to develop generative AI applications (for example, Amazon SageMaker JumpStart; Amazon Bedrock; PartyRock, an Amazon Bedrock Playground; Amazon Q).
• Describe the advantages of using AWS generative AI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives).
• Understand the benefits of AWS infrastructure for generative AI applications (for example, security, compliance, responsibility, safety).
• Understand cost tradeoffs of AWS generative AI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).

2.3 summary: This section focuses on the AWS tools that simplify the path to generative AI applications. You will explore services like Amazon Bedrock and SageMaker JumpStart, and see how they provide a low-barrier entry point for building AI-driven solutions.

You will also learn to assess AWS infrastructure advantages such as security and compliance, while balancing considerations like cost, availability, and performance. This prepares you to recommend AWS solutions that are both technically and economically sound.

Domain 3: Applications of Foundation Models (28% of the exam)

Task Statement 3.1: Describe design considerations for applications that use foundation models.

• Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length).
• Understand the effect of inference parameters on model responses (for example, temperature, input/output length).
• Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock, knowledge base).
• Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB [with MongoDB compatibility], Amazon RDS for PostgreSQL).
• Explain the cost tradeoffs of various approaches to foundation model customization (for example, pre-training, fine-tuning, in-context learning, RAG).
• Understand the role of agents in multi-step tasks (for example, Agents for Amazon Bedrock).

3.1 summary: This section helps you weigh important design choices when using foundation models. You will investigate how pre-trained models are selected, how inference parameters influence output, and how modern techniques like Retrieval Augmented Generation enhance knowledge integration.

Additionally, you will connect these considerations with AWS services that provide vector storage and embedding management. This gives you a broader view of both the technical and financial tradeoffs that support practical model customization.

Task Statement 3.2: Choose effective prompt engineering techniques.

• Describe the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space).
• Understand techniques for prompt engineering (for example, chain-of thought, zero-shot, single-shot, few-shot, prompt templates).
• Understand the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments).
• Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).

3.2 summary: This section develops your knowledge of prompt engineering and how it shapes the output of foundation models. You will experiment conceptually with approaches like zero-shot, few-shot, and chain-of-thought prompts, and discover how prompts can act as the steering wheel of generative workflows.

Beyond techniques, you will learn best practices that ensure prompts deliver quality and reliable output while keeping risks under control. This positions you to design effective prompts that enhance generative AI’s potential while avoiding common pitfalls.

Task Statement 3.3: Describe the training and fine-tuning process for foundation models.

• Describe the key elements of training a foundation model (for example, pre-training, fine-tuning, continuous pre-training).
• Define methods for fine-tuning a foundation model (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training).
• Describe how to prepare data to fine-tune a foundation model (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).

3.3 summary: This section covers how foundation models are adapted for specific use cases. You will see how options like fine-tuning and continuous pre-training maximize impact by aligning models more closely with domain-specific needs.

You will also learn the importance of preparing curated, representative datasets as the foundation of fine-tuning. This knowledge ensures you can recognize when and how to create customized AI applications with confidence.

Task Statement 3.4: Describe methods to evaluate foundation model performance.

• Understand approaches to evaluate foundation model performance (for example, human evaluation, benchmark datasets).
• Identify relevant metrics to assess foundation model performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore).
• Determine whether a foundation model effectively meets business objectives (for example, productivity, user engagement, task engineering).

3.4 summary: This section emphasizes assessment of generative AI solutions. You will learn about key benchmarks and evaluation approaches that provide measurable ways to compare models and ensure they meet technical expectations.

You will also consider how evaluation connects to business metrics such as user satisfaction and productivity. This ensures that your model evaluation skills account for both quality of outputs and alignment with organizational objectives.

Domain 4: Guidelines for Responsible AI (14% of the exam)

Task Statement 4.1: Explain the development of AI systems that are responsible.

• Identify features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity).
• Understand how to use tools to identify features of responsible AI (for example, Guardrails for Amazon Bedrock).
• Understand responsible practices to select a model (for example, environmental considerations, sustainability).
• Identify legal risks of working with generative AI (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations).
• Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets).
• Understand effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting).
• Describe tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon Augmented AI [Amazon A2I]).

4.1 summary: This section guides you through the values and practices that make AI systems responsible. You will identify characteristics like fairness, inclusivity, and safety, and learn how AWS tools can be used to embed these principles from the start.

It also highlights the importance of diverse and well-curated data and explains ways to monitor systems for bias or unintended behavior. This knowledge builds your ability to promote trustworthy, sustainable AI practices.

Task Statement 4.2: Recognize the importance of transparent and explainable models.

• Understand the differences between models that are transparent and explainable and models that are not transparent and explainable.
• Understand the tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, open source models, data, licensing).
• Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance).
• Understand principles of human-centered design for explainable AI.

4.2 summary: This section builds your awareness of transparency within AI systems. You will explore methods and tools that provide insights into how models generate outcomes and recognize the benefits of explainability for business users.

You will also evaluate tradeoffs between performance and interpretability, developing a more nuanced view of how explainability supports trust and adoption in AI solutions.

Domain 5: Security, Compliance, and Governance for AI Solutions (14% of the exam)

Task Statement 5.1: Explain methods to secure AI systems.

• Identify AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model).
• Understand the concept of source citation and documenting data origins (for example, data lineage, data cataloging, SageMaker Model Cards).
• Describe best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity).
• Understand security and privacy considerations for AI systems (for example, application security, threat detection, vulnerability management, infrastructure protection, prompt injection, encryption at rest and in transit).

5.1 summary: This section emphasizes the pillars of security and privacy for AI systems. You will connect AWS services with common practices that protect sensitive data and ensure responsible use of models.

You will also learn about documenting origins of datasets and maintaining data integrity, giving you tools to build confidence and accountability into your AI deployments.

Task Statement 5.2: Recognize governance and compliance regulations for AI systems.

• Identify regulatory compliance standards for AI systems (for example, International Organization for Standardization [ISO], System and Organization Controls [SOC], algorithm accountability laws).
• Identify AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor).
• Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention).
• Describe processes to follow governance protocols (for example, policies, review cadence, review strategies, governance frameworks such as the Generative AI Security Scoping Matrix, transparency standards, team training requirements).

5.2 summary: This section sharpens your knowledge of compliance frameworks and governance responsibilities in AI. You will explore standards and regulatory expectations, and understand how AWS features support adherence to these requirements.

You will also develop awareness of governance strategies that keep AI projects sustainable. This empowers you to understand compliance and governance not as obstacles but as foundational practices that strengthen trust in AI solutions.