AWS AIF-C01 Exam Prep
Key Points by Section
Review just the key points from each section's practice questions. You can filter sections by the accuracy of your answers, so you can spot weak areas quickly.
Section List (All 48 Sections)
AI and ML Fundamentals #1
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #1
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #1
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #1Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #1Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #2Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #2Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #2Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #2Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #2Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #3Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #3Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #3Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #3Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #3Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #4Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #4Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #4Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #4Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #4Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #5Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #5Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #5Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #5Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #5Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #6Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #6Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #6Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #6Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #6Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #7Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #7Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #7Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #7Premium
Review responsible AI: characteristics such as fairness, explainability, transparency, and robustness; bias detection, guardrails, and interpretability; and the legal risks of generative AI.
Security, Compliance, and Governance #7Premium
Review the shared responsibility model; protecting AI systems with IAM, encryption, and PrivateLink; audit and compliance services; and data governance and standards.
AI and ML Fundamentals #8Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #8Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #8Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
AI and ML Fundamentals #9Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #9Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #9Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
AI and ML Fundamentals #10Premium
Review the relationship between AI, ML, and deep learning; supervised, unsupervised, and reinforcement learning; data types; inference modes; the ML lifecycle; and the main AWS AI services.
Generative AI Fundamentals #10Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #10Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Generative AI Fundamentals #11Premium
Review core concepts such as tokens, embeddings, foundation models, and diffusion models; the use cases, benefits, and limitations of generative AI; and generative AI infrastructure such as Bedrock.
Applications of Foundation Models #11Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Applications of Foundation Models #12Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Applications of Foundation Models #13Premium
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.