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.
- 240 questions
- 5 domains
- 15 free · 225 premium
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.