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AWS Certification Path

AWS AI Practitioner (AIF-C01) Roadmap

Complete preparation guide for the AWS Certified AI Practitioner (AIF-C01) certification

Welcome! This roadmap covers all domains for the AIF-C01 exam organized into 4 Units with 12 Chapters. Master AI/ML fundamentals, AWS AI services, generative AI, and responsible AI practices.

1

Unit 1: AI & ML Fundamentals

Core concepts, terminology, and machine learning basics

Chapter 1.1 Unit 1

AI & ML Concepts

Supervised, unsupervised, reinforcement learning

AI vs ML vs Deep Learning
Supervised & Unsupervised Learning
Reinforcement Learning
Features, Labels & Datasets
Chapter 1.2 Unit 1

ML Problem Types

Classification, regression, clustering, NLP, CV

Classification & Regression
Clustering & Anomaly Detection
Natural Language Processing (NLP)
Computer Vision & Object Detection
Chapter 1.3 Unit 1

Model Evaluation

Metrics, overfitting, bias-variance tradeoff

Accuracy, Precision, Recall, F1
AUC-ROC & Confusion Matrix
Overfitting & Underfitting
Cross-validation & Hyperparameter Tuning
2

Unit 2: Generative AI & Foundation Models

LLMs, prompt engineering, RAG, and generative AI on AWS

Chapter 2.1 Unit 2

Foundation Models & LLMs

Large language models, tokens, and inference

Transformer Architecture & Tokens
Pre-training vs Fine-tuning
Amazon Titan, Claude, Llama models
Multimodal Models (text, image, audio)
Chapter 2.2 Unit 2

Prompt Engineering

Prompt techniques and optimization strategies

Zero-shot & Few-shot Prompting
Chain-of-Thought Reasoning
Temperature & Top-P Sampling
System Prompts & Context Windows
Chapter 2.3 Unit 2

RAG & Amazon Bedrock

Retrieval-Augmented Generation and Bedrock APIs

RAG Pattern & Vector Databases
Amazon Bedrock Knowledge Bases
Bedrock Agents & Action Groups
Bedrock Guardrails & Model Evaluation
3

Unit 3: AWS AI/ML Services

Managed AI services, SageMaker, and MLOps on AWS

Chapter 3.1 Unit 3

Managed AI Services

Pre-built AI services for common use cases

Comprehend (NLP & Sentiment)
Rekognition (Image & Video)
Textract, Transcribe & Polly
Translate, Lex, Kendra & Personalize
Chapter 3.2 Unit 3

Amazon SageMaker

Build, train, and deploy ML models at scale

SageMaker Studio & Notebooks
Training Jobs & Hyperparameter Tuning
Real-time & Batch Inference Endpoints
Feature Store, Pipelines & MLflow
Chapter 3.3 Unit 3

Amazon Q & Productivity AI

AI assistants and developer productivity tools

Amazon Q Business
Amazon Q Developer (code assistant)
Forecast & Personalize
SageMaker Ground Truth (labeling)
4

Unit 4: Responsible AI, Security & Governance

Ethical AI, compliance, bias mitigation, and data privacy

Chapter 4.1 Unit 4

Responsible AI Principles

Fairness, transparency, and human-centred AI

Bias Detection & Mitigation
Explainability & SageMaker Clarify
Hallucination & Mitigation Strategies
AWS AI Service Cards
Chapter 4.2 Unit 4

Security & Privacy

Data protection, IAM, and encryption for AI workloads

IAM Roles for AI Services
Amazon Macie & PII Detection
Encryption at Rest & In Transit
VPC Endpoints for AI Services
Chapter 4.3 Unit 4

Governance & Compliance

Model monitoring, audit trails, and regulatory alignment

SageMaker Model Monitor & Drift
CloudTrail Audit Logs for AI APIs
Model Cards & Documentation
GDPR & HIPAA Considerations

AIF-C01 Exam Domains & Weights

24%

Fundamentals of AI & ML

28%

Fundamentals of Generative AI

28%

Applications of Foundation Models

20%

Responsible AI & Security

Focus on the middle two domains — they carry 56% of the exam!

💡 Exam Tip: Focus on recognising which AWS AI service fits which use case. Use AWS Skill Builder free labs and experiment directly with Amazon Bedrock!

Study Resources & Preparation

Official AWS Resources

  • • AWS Skill Builder: AIF-C01 learning plan (free)
  • • AWS Bedrock Workshop: Hands-on generative AI labs
  • • AWS Documentation: Each AI service deep dive
  • • AWS AI Service Cards: Responsible AI documentation

Practice & Courses

  • • Stephane Maarek: AWS AI Practitioner Course (Udemy)
  • • Tutorials Dojo: AIF-C01 Practice Exams
  • • A Cloud Guru: AI Practitioner learning path
  • • AWS Free Tier: Experiment with Bedrock & SageMaker

Exam Details

  • • Duration: 90 minutes
  • • Questions: 65 (multiple choice & multiple response)
  • • Passing Score: 700/1000
  • • Cost: $100 USD

Key Services to Know

  • • Amazon Bedrock: Central to generative AI questions
  • • Amazon SageMaker: ML lifecycle & MLOps
  • • Amazon Q: Business & developer productivity
  • • Comprehend / Rekognition / Textract: Managed AI APIs