AWS AI Practitioner Certification Exam Syllabus

• 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

Objectives:

• 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, costbenefit 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).

Objectives:

• 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.

Objectives:

• 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).

Objectives:

• 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).

Objectives:

• 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).

• 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).

Objectives:

• 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-ofthought, 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).

Objectives:

• 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]).

Objectives:

• 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.

Objectives:

• 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).

Objectives:

• 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).

Objectives:

• 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).

Objectives:

• 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).