AWS Certified AI Practitioner

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AWS Certified AI Practitioner Training covers foundational knowledge of AI, ML & Generative AI services on AWS, including Amazon Bedrock, SageMaker & responsible AI practices. This training is crucial for professionals aiming to build AI-driven solutions & understand ethical AI in cloud environments. Ideal for developers, data analysts, cloud practitioners & beginners in AI, it helps validate essential skills through a 90-minute certification exam with 65 questions.

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1: Introduction to AWS AI Practitioner Certification

• Overview of the AWS Certified AI Practitioner Exam
• Domains, weightage, and question types
• Scoring & passing criteria
• How the exam maps to AI/ML/GenAI job roles
Assignment:
• Read AWS AIF-C01 Exam Guide and list the domains with percentages
• Create a personal exam preparation plan

Module 2: AI & ML Fundamentals

• Diderence between AI, Machine Learning, Deep Learning
• ML types: Supervised, Unsupervised, Reinforcement Learning
• Common ML concepts: models, training, validation, overfitting
• ML lifecycle: data collection → training → evaluation → deployment
Assignment:
• Identify 3 real-world examples for each ML type (Supervised, Unsupervised, RL)
• Short quiz on AI/ML fundamentals

Module 3: Generative AI Concepts

• What is Generative AI?
• Large Language Models (LLMs) and Foundation Models
• Prompt engineering basics (temperature, top-p, tokens)
• Risks in GenAI: hallucinations, bias
• Retrieval-Augmented Generation (RAG) basics
• Use cases: chatbots, summarization, image generation
Mini Project:
• Use Amazon Bedrock to generate text from prompts and experiment with
parameters

Module 4: AWS AI & ML Services Overview

Foundation Model Services
• Amazon Bedrock (Model invocation, Guardrails, Agents)
• Amazon Q (business data assistant)
ML Development Services
• Amazon SageMaker Studio, Canvas, JumpStart
• SageMaker Ground Truth (labeling)
AI Services
• Amazon Rekognition (image/video analysis)
• Amazon Polly (text-to-speech)
• Amazon Transcribe (speech-to-text)
• Amazon Translate (language translation)
• Amazon Comprehend (NLP)
• Amazon Textract (document processing)
• Amazon Lex (chatbots)
Assignment:
• Match AWS services to given AI/ML use cases

Module 5: Responsible AI & Governance

• Principles: fairness, transparency, explainability
• Guardrails in Amazon Bedrock
• Data privacy in AI workloads
• Ethics and bias mitigation strategies
Mini Project:
• Build a sentiment analysis app using Amazon Comprehend with anonymized data

Module 6: Security & Compliance in AI

• AWS Shared Responsibility Model for AI workloads
• IAM roles and policies for AI services
• Encryption and data protection
• Logging and monitoring AI workloads (CloudTrail, CloudWatch)
• Compliance standards: GDPR, HIPAA, SOC
Assignment:
• Create an IAM policy for an AI developer role

Module 7: Integrating AI into Applications

• Using APIs and SDKs for AWS AI services
• Building serverless AI apps (Lambda + AI Services)
• Multi-service integration (e.g., Transcribe + Translate + Polly for multi-language
audio)
Mini Project:
• Create a voice-enabled chatbot using Lex, Polly, and Lambda

Module 8: Cost Management for AI/ML

• Pricing models for Bedrock, SageMaker, AI services
• Cost estimation tools (AWS Pricing Calculator)
• Optimizing costs for AI workloads
Assignment:
• Estimate cost for a SageMaker training job using the pricing calculator

Module 9: Exam Preparation

• Review key concepts from each domain
• Practice exam sessions (AWS Skill Builder, Tutorials Dojo)
• Time management during the exam
• Analyzing wrong answers

Hands-On Projects

1. GenAI Knowledge Base Chatbot (Bedrock + RAG + S3 + Lambda)
2. Image Moderation Pipeline (Rekognition + SNS + S3)
3. Document Automation Workflow (Textract + Comprehend + DynamoDB)
4. AI-powered Voice Assistant (Lex + Polly + Transcribe)
5. No-code ML App (SageMaker Canvas for a prediction use case)

Suggested Training Duration (4–6 Weeks)

Week	Topics	Deliverables
1	AI/ML Fundamentals + GenAI Basics	Assignments + Bedrock Prompt Lab
2	AWS AI Services Overview	AI service mapping quiz
3	Responsible AI + Security	Sentiment Analysis Project
4	Integration & Cost Management	Chatbot or Voice Assistant Project
5	Capstone Project	Choose from project list

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AWS Certified AI Practitioner Course Certification With Training In Bangalore

Q: Module 4: AWS AI & ML Services Overview

Foundation Model Services • Amazon Bedrock (Model invocation, Guardrails, Agents) • Amazon Q (business data assistant) ML Development Services • Amazon SageMaker Studio, Canvas, JumpStart • SageMaker Ground Truth (labeling) AI Services • Amazon Rekognition (image/video analysis) • Amazon Polly (text-to-speech) • Amazon Transcribe (speech-to-text) • Amazon Translate (language translation) • Amazon Comprehend (NLP) • Amazon Textract (document processing) • Amazon Lex (chatbots) Assignment: • Match AWS services to given AI/ML use cases

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