AWS AIF-C01 Exam Prep
Questions by Section
Check the key points of each section with exam-style multiple-choice questions. Work through them in order or at random, and filter by accuracy whenever you need to.
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 #1
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 #1
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 #2
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 #2
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 #2
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #2
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 #2
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 #3
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 #3
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 #3
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #3
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 #3
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 #4
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 #4
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 #4
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #4
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 #4
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 #5
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 #5
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 #5
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.
Responsible AI #5
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 #5
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 #6
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 #6
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 #6
Review model selection criteria, inference parameters, RAG, prompt engineering, fine-tuning, agents, and model evaluation.