AWS Certified AI Practitioner (AIF-C01) Exam Prep

What's inside

1. Level 1: Exam Blueprint and Core AI TerminologyFree
   [Fundamentals of AI and ML] Sets out the exam facts for this independent, no-pass-promised course (exam guide version 1.1: 65 questions with 50 scored and 15 unscored, 90 minutes, scaled passing score 700, four question types, five weighted domains, out-of-scope tasks) and then defines the basic terms of task statement 1.1: AI, ML, deep learning, neural networks, computer vision, NLP, models, algorithms, training, inferencing, LLMs, GenAI and agentic AI.
2. Level 2: Data Types, Learning Types and InferencingFree
   [Fundamentals of AI and ML] Completes task statement 1.1 by classifying the data that models use (labeled and unlabeled, tabular, time-series, image, text, structured and unstructured), contrasting supervised, unsupervised and reinforcement learning, and distinguishing batch from real-time inferencing.
3. Level 3: Right Tool or Wrong Tool: Use Cases and AI Services
   [Fundamentals of AI and ML] Covers task statement 1.2: where AI/ML adds value, when it is the wrong tool (for example when a specific deterministic outcome is needed or the cost outweighs the benefit), when a traditional ML technique such as regression, classification or clustering beats a foundation model, and what each managed AI service is for (Amazon Comprehend, Lex, Polly, Rekognition, Textract, Transcribe, Translate, Personalize and SageMaker AI).
4. Level 4: The ML Lifecycle, MLOps and Model Metrics
   [Fundamentals of AI and ML] Covers task statement 1.3 at recognition level, with no pipeline building: the stages of the ML pipeline from data collection to monitoring, sources of models (open source pre-trained versus custom training), managed API versus self-hosted deployment, the SageMaker AI capabilities that serve each stage, MLOps concepts, and model versus business metrics.
5. Level 5: GenAI Building Blocks: Tokens to Foundation Models
   [Fundamentals of GenAI] Covers the core concepts of task statement 2.1: tokens, chunking, embeddings and vectors, transformer-based LLMs, foundation models, multi-modal and diffusion models, typical GenAI use cases, and the foundation model lifecycle from data and model selection through pre-training, fine-tuning, evaluation, deployment and feedback.
6. Level 6: Context Engineering, Agentic AI and MCP
   [Fundamentals of GenAI] Finishes task statement 2.1 with the concepts added in guide version 1.1: prompt engineering versus context engineering, the context window, what makes a system agentic (reasoning, planning, tool use, memory, multi-step and multi-agent workflows), and the role of Model Context Protocol (MCP) in connecting models to tools and data.
7. Level 7: GenAI Strengths, Limits and Business Value
   [Fundamentals of GenAI] Covers task statement 2.2: the advantages of GenAI (adaptability, responsiveness, simplicity), its disadvantages (hallucinations, interpretability, inaccuracy, nondeterminism), the factors for choosing a suitable model (model type, performance requirements, capabilities, constraints, compliance), and the business value metrics used to judge a GenAI application.
8. Level 8: AWS GenAI Services and Their Cost Tradeoffs
   [Fundamentals of GenAI] Covers task statement 2.3: what each AWS GenAI building block is for (Amazon Bedrock, Amazon Nova, Amazon SageMaker AI and SageMaker JumpStart, Amazon Quick, Kiro, Strands Agents, Amazon Bedrock AgentCore, AWS Transform), the compute and container services workloads can run on (Amazon EC2, AWS Lambda, Amazon ECS, Amazon EKS), the benefits of AWS infrastructure, and cost tradeoffs such as token-based pricing, provisioned throughput, responsiveness, availability and Regional coverage.
9. Level 9: Selecting an FM, Inference Parameters and Agents
   [Applications of Foundation Models] Opens task statement 3.1 with the design choices for an FM-powered application: selection criteria for pre-trained models (cost, modality, latency, multi-lingual support, model size and complexity, customization, input and output length), the effect of inference parameters such as temperature and response length, and the role of agents in multi-step tasks, including Amazon Bedrock Agents and their business uses.
10. Level 10: RAG, Knowledge Bases and Vector Stores
    [Applications of Foundation Models] Continues task statement 3.1 with Retrieval Augmented Generation: how RAG grounds responses in company data, how Amazon Bedrock Knowledge Bases implements it, which AWS services can store embeddings (Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon RDS for PostgreSQL, Amazon DocumentDB), and how they differ from the other in-scope databases (Amazon DynamoDB, Amazon ElastiCache).
11. Level 11: Customizing Foundation Models: Cost and Data
    [Applications of Foundation Models] Completes task statement 3.1 and covers task statement 3.3 at concept level: the cost and effort ladder from in-context learning through RAG, fine-tuning and model distillation to pre-training, the methods of fine-tuning (instruction tuning, domain adaptation, transfer learning, continuous pre-training), and how to prepare fine-tuning data (curation, governance, size, labeling, representativeness).
12. Level 12: Prompt Engineering Techniques and Risks
    [Applications of Foundation Models] Covers task statement 3.2: the parts of a prompt (instruction, context, negative prompts), techniques (zero-shot, single-shot, few-shot, chain-of-thought, prompt templates), best practices such as specificity, experimentation and guardrails, managing and versioning prompts with Amazon Bedrock Prompt Management, and the risks of prompt engineering (exposure, poisoning, hijacking, jailbreaking).
13. Level 13: Evaluating Foundation Model Performance
    [Applications of Foundation Models] Covers task statement 3.4: evaluation approaches (human evaluation, benchmark datasets, Amazon Bedrock Model Evaluation, LLM-as-a-judge), the metrics ROUGE, BLEU and BERTScore and the tasks each suits, and how to judge whether an FM, RAG application or agent meets business objectives such as productivity, user engagement and task completion.
14. Level 14: Building Responsible AI Systems
    [Guidelines for Responsible AI] Covers task statement 4.1: the features of responsible AI (bias, fairness, inclusivity, robustness, safety, veracity), Amazon Bedrock Guardrails as a responsible-AI tool, responsible and sustainable model selection, legal risks of GenAI (intellectual property infringement, biased outputs, loss of customer trust, end-user risk, hallucinations), dataset characteristics (inclusive, diverse, balanced, curated), the effects of bias and variance (overfitting, underfitting, harm to demographic groups), and tools to detect and monitor bias such as Amazon SageMaker Clarify, Amazon SageMaker Model Monitor and human review with Amazon Augmented AI.
15. Level 15: Transparent and Explainable Models
    [Guidelines for Responsible AI] Covers task statement 4.2: the difference between transparent, explainable models and opaque ones, tools that document and assess models (Amazon SageMaker Model Cards, Amazon Bedrock Model Evaluations, open source models, data and licensing), the tradeoffs between model safety and transparency such as interpretability versus performance, and the principles of human-centered design for explainable AI.
16. Level 16: Securing AI Systems and Their Data
    [Security, Compliance, and Governance for AI Solutions] Covers task statement 5.1 at recognition level, not implementation: the shared responsibility model for AI, IAM roles, policies and permissions, encryption with AWS KMS, Amazon Macie for sensitive data discovery, AWS Secrets Manager, private connectivity with Amazon VPC and AWS PrivateLink, Amazon CloudFront, identity and policy controls for agents in Amazon Bedrock AgentCore, source citation and data lineage, secure data engineering with the in-scope analytics and storage services (AWS Glue, AWS Glue DataBrew, AWS Lake Formation, AWS Data Exchange, Amazon EMR, Amazon Redshift, Amazon S3, Amazon S3 Glacier), and threats such as prompt injection.
17. Level 17: Governance and Compliance for AI Solutions
    [Security, Compliance, and Governance for AI Solutions] Covers task statement 5.2 at recognition level: regulatory compliance standards for AI, the services that support governance and compliance (AWS Config, Amazon Inspector, AWS Artifact, AWS CloudTrail, Amazon CloudWatch, AWS Trusted Advisor, AWS Well-Architected Tool), cost governance with AWS Budgets and AWS Cost Explorer, data governance strategies (lifecycle, logging, residency, monitoring, retention), and governance processes including the Generative AI Security Scoping Matrix.
18. Level 18: Timed Mock Exam: 65 Questions in 90 Minutes Timed mock
    A timed 65-question, 90-minute mock of original practice questions (not real exam questions) that tests all five AIF-C01 domains in proportion to their weightings: Fundamentals of AI and ML 20%, Fundamentals of GenAI 24%, Applications of Foundation Models 28%, Guidelines for Responsible AI 14%, and Security, Compliance, and Governance for AI Solutions 14%.

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The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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