AWS AI Practitioner Study Library
AIF-C01
21 free articles on the concepts the AIF-C01 exam tests, grouped by exam domain. Each one backs real questions in our practice exam.
Applications of Foundation Models
- How to Choose a Foundation Model: Cost, Latency, Size, Modality, Languages, and Context Window How to choose a foundation model by weighing cost, latency, model size, modality, language support, and context window against real workload needs.
- Preparing Data to Fine-Tune a Foundation Model: Curation, Size, and Representativeness What it takes to prepare fine-tuning data for a foundation model: curation, governance, dataset size, labeling, and representativeness explained.
- Prompt Injection, Hijacking, Poisoning, and Exposure: Prompt-Level Risks Explained How prompt injection, hijacking, poisoning, and exposure attacks work against foundation models, how they differ, and how AWS tools reduce each risk.
- Transfer Learning and Catastrophic Forgetting: Reusing Model Knowledge Without Losing It Transfer learning lets a model reuse knowledge from one task to learn a related one faster — and catastrophic forgetting is the risk that comes with it.
- What Is RLHF? Reinforcement Learning from Human Feedback, Step by Step RLHF aligns a foundation model with human preferences: reviewers rank outputs, a reward model learns the rankings, and RL updates the model.
Fundamentals of AI and ML
- Accuracy, Precision, Recall, F1, and AUC: Choosing the Right Model Evaluation Metric Accuracy, precision, recall, F1, and AUC explained with the confusion matrix — and how to pick the right metric for imbalanced or high-risk ML problems.
- AWS Certified AI Practitioner (AIF-C01) Study Guide: Format, Domains, Cost, and How to Pass AIF-C01 exam format — 65 questions, 90 minutes, 700 passing score, $100 fee — all five domains explained, plus a 4-week study plan and exam-day tips.
Fundamentals of Generative AI
- Generative AI vs Traditional Machine Learning: Adaptability, Use Cases, and When Each Wins How generative AI differs from traditional ML: adaptability, one model for many tasks, and when a task-specific predictive model still wins.
- Measuring Generative AI Business Value: Conversion Rate, ARPU, CLV, and Efficiency Learn how conversion rate, ARPU, CLV, customer retention, and efficiency metrics prove whether a generative AI feature is actually paying off.
- Multimodal AI Models Explained: When One Model Handles Text, Images, and Audio What modality means, how multimodal foundation models handle text, images, and audio in one request, and when one model beats a chain of tools.
- The Foundation Model Lifecycle: Data Selection to Deployment, in Order Master the foundation model lifecycle order — data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback.
- What Is a Foundation Model? Pre-Training, Adaptability, and Why One Model Fits Many Tasks What a foundation model is, how pre-training on broad data works, and why one FM can power summarization, chat, and translation without retraining.
Guidelines for Responsible AI
- AI Bias and Fairness: Where Bias Comes From and What It Does to Model Decisions What bias means in responsible AI, how it enters training data and models, the harm it causes, and the AWS tools that help you detect and reduce it.
- Black-Box vs Interpretable Models: The Explainability-Accuracy Tradeoff and When It Matters Which model types are black boxes, which are interpretable, and how to weigh the accuracy-explainability tradeoff by stakes and regulation.
- Generative AI and Copyright: Infringement Risks from Training Data and Generated Output How generative AI creates copyright infringement risk — training data, output that mimics protected work, and the AWS controls that reduce exposure.
- Legal Risks of Generative AI: Hallucination Liability, Harmful Advice, and Compliance Exposure The legal risk categories of generative AI — liability for harmful advice, hallucinations, IP, privacy, and bias — and how to tell them apart fast.
- Overfitting vs Underfitting: Diagnosing Model Fit from Training and Validation Accuracy Read training vs validation accuracy to diagnose overfitting and underfitting, understand why each happens, and pick the fix the scenario calls for.
- What Is Explainable AI? Transparency, Interpretability, and Training-Data Disclosure Explainable AI defined: how transparency, interpretability, and explainability differ, plus the AWS tools that document models and training data.
Security, Compliance, and Governance for AI Solutions
- Data Lineage for AI: Tracking Provenance, Licensing, and Access from Ingestion to Training What data lineage means for AI systems: tracking origin, transformations, licensing, and access of training data — and the AWS tools that record it.
- Privacy-Enhancing Technologies (PETs) for AI: Differential Privacy, Anonymization, and Their Tradeoffs Privacy-enhancing technologies for AI explained: differential privacy, anonymization, masking, and federated learning — plus the utility tradeoffs.
- The EU AI Act and Algorithmic Accountability Laws: Risk Tiers, Bias Audits, and Impact Assessments How the EU AI Act's risk tiers work, what algorithmic accountability laws require, and how bias audits and impact assessments fit AI governance.
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