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AWS Certified AI Practitioner (AIF-C01) Syllabus

Fundamentals of AI and ML

Core AI and ML concepts, practical use cases and the ML development lifecycle.

20%
Weight
13
Questions
20
Marks

AI Concepts and Terminology

  • AI, ML, deep learning, generative AI and agentic AI
  • Supervised, unsupervised and reinforcement learning
  • Batch, real-time, asynchronous and serverless inference
  • Labeled, unlabeled, structured and unstructured data
  • Bias, fairness and model fit

Practical AI Use Cases

  • When AI/ML is and is not appropriate
  • Regression, classification and clustering
  • Amazon Transcribe, Translate, Comprehend, Lex and Polly
  • Traditional ML vs. foundation models

ML Development Lifecycle

  • ML pipeline stages and model sources
  • Managed vs. self-hosted model APIs
  • Amazon SageMaker AI and Amazon Bedrock by lifecycle stage
  • MLOps: monitoring and retraining
  • Accuracy, precision, recall and F1 vs. business metrics

Fundamentals of Generative AI

Generative AI concepts, its business capabilities and limitations, and AWS infrastructure for generative AI.

24%
Weight
16
Questions
24
Marks

Generative AI Concepts

  • Tokens, chunking, embeddings and vectors
  • Transformers, multimodal and diffusion models
  • Foundation model lifecycle
  • Token-based pricing
  • Agentic AI: tool use, memory, orchestration and MCP

Capabilities and Limitations

  • Hallucinations and nondeterminism
  • Interpretability and accuracy limits
  • Model selection by performance, cost, latency and compliance
  • Business value metrics such as ROI and efficiency

AWS Generative AI Services

  • Amazon Bedrock and Amazon SageMaker JumpStart
  • Amazon Bedrock AgentCore and Strands Agents
  • Amazon Quick and Kiro
  • Provisioned throughput vs. on-demand pricing
  • Regional availability and cost trade-offs

Applications of Foundation Models

Designing foundation model applications, prompt engineering, fine-tuning and evaluating model performance.

28%
Weight
18
Questions
28
Marks

Design Considerations and RAG

  • Foundation model selection criteria
  • Inference parameters such as temperature
  • Retrieval Augmented Generation with Amazon Bedrock Knowledge Bases
  • Vector stores: OpenSearch Service, Aurora, Neptune and RDS for PostgreSQL
  • Cost trade-offs of in-context learning, RAG, fine-tuning and distillation
  • AI agents for business tasks

Prompt Engineering

  • Context, instructions and negative prompts
  • Zero-shot, few-shot and chain-of-thought prompting
  • Prompt templates and Amazon Bedrock Prompt Management
  • Prompt injection, poisoning, hijacking and jailbreaking risks

Training and Fine-Tuning

  • Pre-training, fine-tuning and continued pre-training
  • Instruction tuning and domain adaptation
  • Data curation, labeling and RLHF
  • Model distillation

Evaluating Foundation Models

  • Human evaluation and benchmark datasets
  • Amazon Bedrock Model Evaluation
  • ROUGE, BLEU and BERTScore
  • LLM-as-a-judge
  • Evaluating RAG and agent workflows

Guidelines for Responsible AI

Developing responsible AI systems and recognising the importance of transparent, explainable models.

14%
Weight
9
Questions
14
Marks

Responsible AI Development

  • Bias, fairness, inclusivity, robustness and safety
  • Amazon Bedrock Guardrails
  • Legal risks: IP infringement, biased output and hallucinations
  • Dataset diversity and balance
  • Overfitting, underfitting and subgroup analysis

Transparency and Explainability

  • Transparent vs. opaque models
  • Amazon SageMaker Model Cards
  • Safety, transparency and performance trade-offs
  • Human-centered design and user feedback

Security, Compliance, and Governance for AI Solutions

Securing AI systems and recognising governance and compliance requirements for AI on AWS.

14%
Weight
9
Questions
14
Marks

Securing AI Systems

  • IAM, encryption and AWS PrivateLink
  • Amazon Macie for sensitive data
  • Shared responsibility model for AI
  • Prompt injection and data leakage threats
  • Data lineage and source citation

AI Governance and Compliance

  • AWS Config and Amazon Inspector
  • AWS Artifact, AWS CloudTrail and AWS Trusted Advisor
  • Data residency, retention and logging
  • Generative AI Security Scoping Matrix
  • Governance policies and review cadence

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Learning Progress

Fundamentals of AI and ML 85%
Fundamentals of Generative AI 92%

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