AIF-C01 Preparation Details
Preparing for the AIF-C01 AWS Certified AI Practitioner certification exam? Start here with a complete, objective-wise AIF-C01 study guide designed to help you pass faster.
This guide brings together official AWS documentation, key concepts, and curated resources for every AIF-C01 exam objective, making it ideal for both beginners and last-minute revision.
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AWS Generative AI Developer Prep
| Coursera | AWS Certified AI Practitioner (Pearson) |
| Udemy | Ultimate AWS Certified AI Practitioner |
| Whizlabs | AWS Certified AI Practitioner Practice Test |
Content Domain 1: Fundamentals of AI and ML
Task Statement 1.1: Explain basic AI concepts and terminologies
Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, NLP, model, algorithm, training and inferencing, bias, fairness, fit, LLM, GenAI, agentic AI)
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Machine Learning Lens – AWS Well-Architected Framework
Types of Algorithms – Amazon SageMaker AI
Describe the similarities and differences between AI, ML, GenAI, deep learning, and agentic AI
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Generative AI Lens – AWS Well-Architected Framework
Machine Learning Lens – AWS Well-Architected Framework
Describe various types of inferencing (for example, batch, real-time, asynchronous, serverless)
Batch inference – Amazon Bedrock
Amazon Bedrock model inference
Deploy a model in Amazon SageMaker
Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured)
Types of Algorithms – Amazon SageMaker AI
Supported multimodal input for Amazon Bedrock models
Data Wrangler – Amazon SageMaker AI
Describe different types of AI/ML learning (for example, supervised learning, unsupervised learning, reinforcement learning methods)
Types of Algorithms – Amazon SageMaker AI
Built-in algorithms and pretrained models in Amazon SageMaker
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
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)
Machine Learning Lens – AWS Well-Architected Framework
Machine Learning (ML) and Artificial Intelligence (AI) – Overview of Amazon Web Services
Determine when AI/ML solutions are not appropriate (for example, cost-benefit analyses, situations when a specific outcome is needed instead of a prediction)
Choosing an AWS machine learning service
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Select the appropriate AI/ML techniques for specific use cases (for example, regression, classification, clustering)
Types of Algorithms – Amazon SageMaker AI
Built-in algorithms and pretrained models in Amazon SageMaker
Machine Learning Lens – AWS Well-Architected Framework
Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting, knowledge bases, agentic AI)
Machine Learning (ML) and Artificial Intelligence (AI) – Overview of Amazon Web Services
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Explain the capabilities of AWS managed AI/ML services (for example, Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly)
Amazon Machine Learning Documentation
Choosing an AWS machine learning service
Identify when traditional ML models or foundation models (FMs) are appropriate for a specific use case (for example, based on regulatory concerns, explainability requirements, operational constraints)
Choosing an AWS machine learning service
Choosing a generative AI service – AWS Decision Guides
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
Task Statement 1.3: Describe the AI/ML development lifecycle
Describe and differentiate components of an AI/ML pipeline
Amazon SageMaker Pipelines – create and manage ML workflows
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Machine Learning Lens – AWS Well-Architected Framework
Describe sources of FM models (for example, open source pre-trained models, training custom models)
Supported foundation models in Amazon Bedrock
Customize an Amazon Bedrock model by fine-tuning
Describe methods to use a model in production (for example, managed API service, self-hosted API)
Deploy a model in Amazon SageMaker
Choosing an AWS machine learning service
Identify relevant AWS services and features for each stage of an AI/ML pipeline (for example, Amazon Bedrock, Amazon Q, Amazon Quick, Kiro, SageMaker AI)
Amazon SageMaker Pipelines – create and manage ML workflows
SageMaker Data Wrangler – Amazon SageMaker AI
Describe fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training)
Amazon SageMaker Model Monitor
Amazon SageMaker Pipelines – create and manage ML workflows
Amazon SageMaker Model Registry
Machine Learning Lens – AWS Well-Architected Framework
Describe model performance metrics (for example, accuracy, precision, recall, F1 score) and business metrics (for example, cost per user, development costs, customer feedback, ROI) to evaluate ML models
Metrics and validation – Amazon SageMaker AI
Accuracy – Amazon SageMaker AI
Choose the best performing model using Amazon Bedrock evaluations
Prepare – Machine Learning Lens
Content Domain 2: Fundamentals of GenAI
Task Statement 2.1: Explain the basic concepts of generative AI (GenAI)
Define foundational GenAI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, FMs, multi-modal models, diffusion models)
Key terminology – Amazon Bedrock
Prompt engineering concepts – Amazon Bedrock
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Configure chunking for Amazon Bedrock Knowledge Bases
Titan Embeddings G1 – Amazon Bedrock
Identify potential use cases for GenAI models (for example, image, video, and audio generation; summarization; AI assistants; translation; code generation; customer service agents; search; recommendation engines)
Video generation with Sora 2 – Amazon Bedrock
Describe the FM lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback)
Customize an Amazon Bedrock model by fine-tuning
Choose the best performing model using Amazon Bedrock evaluations
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Generative AI Lens – AWS Well-Architected Framework
Describe the token-based pricing model and its effect on cost and performance for inference
Monitor your token usage by counting tokens before running inference – Amazon Bedrock
Model execution strategies for AI workloads – AWS Prescriptive Guidance
Describe the role of context engineering in FM applications
Prompt engineering concepts – Amazon Bedrock
Architecting a successful generative AI proof of concept – AWS Prescriptive Guidance
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Define foundational agentic AI concepts (for example, multi-agent system patterns, MCP and its role in connecting agents to external systems, multi-agent communication patterns, memory management, tool usage, and workflow orchestration)
Overview – Amazon Bedrock AgentCore
Amazon Bedrock AgentCore Gateway: Securely connect tools and other resources
Amazon Bedrock AgentCore Memory
Guidance for Multi-Agent Orchestration on AWS
Task Statement 2.2: Understand the capabilities and limitations of GenAI for solving business problems
Describe the advantages of GenAI (for example, adaptability, responsiveness, conversational capabilities, ability to generate content)
Choosing a generative AI service – AWS Decision Guides
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Identify disadvantages of GenAI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism)
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Prompt engineering concepts – Amazon Bedrock
Generative AI Lens – AWS Well-Architected Framework
Identify factors to consider when selecting GenAI models (for example, model types, performance requirements, capabilities, constraints, compliance, cost, latency, model complexity)
Choosing a generative AI service – AWS Decision Guides
Amazon Bedrock or Amazon SageMaker AI?
Supported foundation models in Amazon Bedrock
Understanding intelligent prompt routing in Amazon Bedrock
Determine business value and metrics for GenAI applications (for example, cross-domain performance, ROI, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value)
Generative AI quality metrics – Generative AI Lens
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Choose the best performing model using Amazon Bedrock evaluations
Task Statement 2.3: Describe AWS infrastructure and technologies for building GenAI applications
Identify AWS services and features to develop GenAI applications (for example, Amazon Bedrock, Amazon SageMaker AI, SageMaker JumpStart, Amazon Q, Kiro, Strands Agents, Amazon Bedrock AgentCore)
Overview – Amazon Bedrock AgentCore
Amazon Bedrock or Amazon SageMaker AI?
Describe the advantages of using AWS GenAI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives)
Choosing a generative AI service – AWS Decision Guides
Model execution strategies for AI workloads – AWS Prescriptive Guidance
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Describe the benefits of AWS infrastructure for GenAI applications (for example, security, compliance, responsibility, safety)
Implementing security and responsible AI – AWS Prescriptive Guidance
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
Describe cost tradeoffs of AWS GenAI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models)
Increase model invocation capacity with Provisioned Throughput in Amazon Bedrock
Increase throughput with cross-Region inference – Amazon Bedrock
GENCOST03-BP03 Implement prompt caching to reduce token costs – Generative AI Lens
Content Domain 3: Applications of Foundation Models
Task Statement 3.1: Describe design considerations for applications that use foundation models (FMs)
Identify selection criteria to choose FMs (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length, prompt caching)
Supported foundation models in Amazon Bedrock
Choosing a generative AI service – AWS Decision Guides
Understanding intelligent prompt routing in Amazon Bedrock
GENCOST03-BP03 Implement prompt caching to reduce token costs – Generative AI Lens
Describe the effect of inference parameters on model responses (for example, temperature, input/output length)
Inference parameters for foundation models – Amazon Bedrock
Configure and customize queries and response generation – Amazon Bedrock
Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock Knowledge Bases)
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Knowledge bases for Amazon Bedrock – AWS Prescriptive Guidance
Retrieval Augmented Generation options and architectures on AWS – AWS Prescriptive Guidance
Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon RDS for PostgreSQL)
Prerequisites for using a vector store you created for a knowledge base – Amazon Bedrock
Choosing an AWS vector database for RAG use cases – AWS Prescriptive Guidance
Vector database options – AWS Prescriptive Guidance
Amazon OpenSearch Service – vector database capabilities
Explain the cost tradeoffs of various approaches to FM customization (for example, pre-training, fine-tuning, in-context learning, RAG, model distillation)
Customize an Amazon Bedrock model by fine-tuning
Distill a model in Amazon Bedrock
Architecting a successful generative AI proof of concept – AWS Prescriptive Guidance
Define the role of AI agents and describe AI agents’ business applications
Overview – Amazon Bedrock AgentCore
Task Statement 3.2: Choose effective prompt engineering techniques
Define the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts)
What is a prompt? – Amazon Bedrock
Prompt engineering concepts – Amazon Bedrock
Prompt templates and examples for Amazon Bedrock text models
Define techniques for prompt engineering (for example, chain-of-thought, zero-shot, single-shot, few-shot, prompt templates)
Prompt engineering concepts – Amazon Bedrock
Optimizing generative AI prompts – AWS Prescriptive Guidance
Advanced prompt templates – Amazon Bedrock
Identify and describe the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments)
Optimizing generative AI prompts – AWS Prescriptive Guidance
LLM prompt engineering best practices – AWS Prescriptive Guidance
Safeguard your application with Amazon Bedrock Guardrails
Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking)
Prompt injection security – Amazon Bedrock
Detect prompt attacks with Amazon Bedrock Guardrails
4. Input validation and guardrails for agentic AI systems on AWS – AWS Prescriptive Guidance
LLM prompt engineering best practices FAQ – AWS Prescriptive Guidance
Describe prompt versioning and management strategies that use Amazon Bedrock Prompt Management
Construct and store reusable prompts with Prompt management in Amazon Bedrock
Manage versions of your prompt – Amazon Bedrock
Test a prompt using Prompt management – Amazon Bedrock
Task Statement 3.3: Describe the training and fine-tuning process for FMs
Describe the key elements of training an FM (for example, pre-training, fine-tuning, continuous pre-training, distillation)
Customize an Amazon Bedrock model by fine-tuning
Customize a model with fine-tuning or continued pre-training in Amazon Bedrock
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Define methods for fine-tuning an FM (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training)
Customize a model with fine-tuning or continued pre-training in Amazon Bedrock
Fine-tune Amazon Bedrock models
Describe how to prepare data to fine-tune an FM (for example, data curation, governance, size, labeling, representativeness, RLHF)
Prepare the fine-tuning training and validation datasets – Amazon Bedrock
Amazon SageMaker Data Wrangler
Amazon SageMaker Ground Truth – Create datasets
Amazon Augmented AI (Amazon A2I) Developer Guide
Task Statement 3.4: Describe methods to evaluate FM performance
Determine approaches to evaluate FM performance (for example, human-in-the-loop evaluation, benchmark datasets, Amazon Bedrock Model Evaluation)
Choose the best performing model using Amazon Bedrock evaluations
Create a human evaluation job – Amazon Bedrock
Create an evaluation job – Amazon Bedrock
Identify relevant metrics to assess FM performance (for example, ROUGE, BLEU, BERTScore, LLM-as-a-judge)
Review metrics for an automated model evaluation job in Amazon Bedrock (console)
Use metrics to understand model performance – Amazon Bedrock
Evaluate model performance using another LLM as a judge – Amazon Bedrock
Evaluate an Amazon Bedrock model for text summarization accuracy – Amazon SageMaker AI
Determine whether an FM effectively meets business objectives (for example, productivity, user engagement, task engineering)
Generative AI quality metrics – Generative AI Lens
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Identify approaches to evaluate the performance of applications built with FM (for example, RAG, agents, workflows)
RAG evaluation for Amazon Bedrock Knowledge Bases
Evaluators – Amazon Bedrock AgentCore
Generative AI quality metrics – Generative AI Lens
Identify business objective alignment metrics for AI applications (for example, task completion rate, user satisfaction, cost per interaction)
Generative AI quality metrics – Generative AI Lens
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Content Domain 4: Guidelines for Responsible AI
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)
Responsible AI – Generative AI Lens
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Explain how to use tools to identify features of responsible AI (for example, Amazon Bedrock Guardrails)
Safeguard your application with Amazon Bedrock Guardrails
Create a guardrail – Amazon Bedrock
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Define responsible practices to select a model (for example, environmental considerations, sustainability)
Responsible AI – Generative AI Lens
Choosing a generative AI service – AWS Decision Guides
Implementing security and responsible AI – AWS Prescriptive Guidance
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)
Responsible AI – Generative AI Lens
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets)
Prepare – Machine Learning Lens – AWS Well-Architected Framework
Pre-training Data Bias – Amazon SageMaker AI
Prepare the fine-tuning training and validation datasets – Amazon Bedrock
Describe effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting)
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
Pre-training Data Bias – Amazon SageMaker AI
Post-training Bias – Amazon SageMaker AI
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])
Fairness, model explainability and bias detection with SageMaker Clarify – Amazon SageMaker AI
Amazon SageMaker Model Monitor
Amazon Augmented AI (Amazon A2I) Developer Guide
Monitor bias drift and feature attribution drift – Amazon SageMaker AI
Task Statement 4.2: Recognize the importance of transparent and explainable models
Describe the differences between models that are transparent and explainable and models that are not transparent and explainable
Responsible AI – Generative AI Lens
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
Describe tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, SageMaker Clarify, Amazon Bedrock Model Evaluations, open source models, data, licensing)
Fairness, model explainability and bias detection with SageMaker Clarify – Amazon SageMaker AI
Choose the best performing model using Amazon Bedrock evaluations
Trace the steps that Amazon Bedrock Agents takes
Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance)
Responsible AI – Generative AI Lens
Implementing security and responsible AI – AWS Prescriptive Guidance
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
Describe principles of human-centered design for explainable AI (for example, user-feedback mechanisms, AI decision transparency)
Amazon Augmented AI (Amazon A2I) Developer Guide
Human in the loop for Amazon Bedrock Agents
Responsible AI – Generative AI Lens
Content Domain 5: Security, Compliance, and Governance for AI Solutions
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; Amazon Bedrock AgentCore Identity; Policy in AgentCore; Amazon Bedrock Guardrails)
Amazon Bedrock and data protection
Data protection in Amazon Bedrock AgentCore Identity
Safeguard your application with Amazon Bedrock Guardrails
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
Describe the concept of source citation and documenting data origins (for example, data lineage, data cataloging, Amazon SageMaker Model Cards)
Log Amazon Bedrock API calls with AWS CloudTrail
Describe best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity)
AWS Privacy Reference Architecture – AWS Prescriptive Guidance
Data Protection – Machine Learning Lens
Security perspective: Compliance and assurance of AI systems – AWS Cloud Adoption Framework
Describe 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, data leakage prevention, output filtering and validation, audit trail and logging requirements for AI interactions, toxicity)
Prompt injection security – Amazon Bedrock
Detect prompt attacks with Amazon Bedrock Guardrails
4. Input validation and guardrails for agentic AI systems on AWS – AWS Prescriptive Guidance
Describe hallucination detection methods and grounding techniques to improve output accuracy (for example, RAG grounding, output validation, confidence scoring)
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Grounding and Retrieval Augmented Generation – AWS Prescriptive Guidance
Task Statement 5.2: Recognize governance and compliance regulations for AI systems
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)
Log Amazon Bedrock API calls with AWS CloudTrail
Choosing an AWS cloud governance service – AWS Decision Guides
Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention)
Enterprise-grade security and governance for generative AI applications – AWS Prescriptive Guidance
Security and governance – AWS Prescriptive Guidance
Security perspective: Compliance and assurance of AI systems – AWS Cloud Adoption Framework
Amazon S3 Object Lifecycle Management
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)
Generative AI Lens – AWS Well-Architected Framework
Enterprise-grade security and governance for generative AI applications – AWS Prescriptive Guidance
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI
This brings us to the end of the AIF-C01 AWS Certified AI Practitioner exam study guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you how your preparation is going on!
In case you are preparing for other AWS certification exams, check out the AWS study guides for those exams.
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