Cert Notes/ Commute Study Notes
Roadmap
KOEN
CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
AIF-C01 · FoundationalAI Practitioner - Foundational
  • Week 1
    • 1.The Relationship and Differences Between AI, Machine Learning, and Deep Learning
    • 2.Learning Types: Supervised, Unsupervised, and Reinforcement Learning
    • 3.Problems Where ML Fits vs. Problems Where It Doesn't
    • 4.Key Terms: Model, Training, Inference, Feature, Label, Overfitting
    • 5.Week 1 Wrap-Up: AI/ML Fundamentals Review
  • Week 2
    • 1.ML Lifecycle Overview: One Complete Cycle from Data to Operations
    • 2.Data: Structured vs Unstructured, Data Splitting, and the Power of Quality
    • 3.Model Evaluation Basics: Accuracy, Precision, Recall, and Overfitting/Underfitting
    • 4.The Human Role in ML Development: Labeling, Feedback, and Iterative Improvement
    • 5.Week 2 Summary: ML Lifecycle and Data at a Glance
  • Week 3
    • 1.What Is Generative AI: Difference from Traditional ML, and Foundation Models
    • 2.How LLMs Work: Tokens, Embeddings, Context Window, and Inference
    • 3.Prompt Engineering Basics: Good Prompts, Zero/Few-shot, and Limitations
    • 4.Limitations and Risks of Generative AI: Hallucinations, Bias, Non-determinism, and Appropriate Use Cases
    • 5.Week 3 Comprehensive Review: Wrapping Up Generative AI Fundamentals at a Glance
  • Week 4
    • 1.Amazon Bedrock: Fully Managed Service for Renting Foundation Models
    • 2.Amazon SageMaker: A Platform for Directly Training and Deploying ML Models
    • 3.AWS AI Services (1): Managed APIs Handling Images, Documents, Text, and Speech
    • 4.AWS AI Services (2) + Amazon Q: Chatbots, Search, Recommendations, Forecasting, and Generative Assistant
    • 5.Week 4 Comprehensive Review: Complete Map of AWS AI/ML Services
  • Week 5
    • 1.Principles of Responsible AI: Fairness, Bias, Transparency, Explainability, Robustness, Privacy
    • 2.AWS's Responsible AI Tools: SageMaker Clarify, Model Monitor, Bedrock Guardrails, AI Service Cards
    • 3.AI Security: Least Privilege IAM, Data Encryption, PII Protection, PrivateLink, Shared Responsibility Model
    • 4.Data Governance and Compliance: Data Origin·Quality, Model Governance, Audit·Logging, Legal and Ethical Considerations for Generative AI
    • 5.Week 5 Comprehensive Review: Binding Responsible AI·Security·Governance into One
  • Week 6
    • 1.Domain Review 1: AI/ML Fundamentals + Generative AI Fundamentals: Critical Summary
    • 2.Domain Review 2: Foundation Model Applications (AWS AI Services) Critical Summary
    • 3.Domain Review 3: Responsible AI + Security·Governance Critical Summary
    • 4.Full Mock Exam Pace: Five Domains Comprehensive Questions
    • 5.D-Day Wrap-Up: Exam Structure, Keyword → Service Translation Table, Frequently-Missed Traps
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
← All certifications/AIF-C01

AI Practitioner - Foundational

6 weeks · 30 days · Foundational

Week 1 is available in English as a free preview. The full course is currently Korean-only — view the Korean track.

Start with Week 1

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Exam Information

입문 · 사전지식 불필요
Questions
65
Duration
90min
Passing
700 / 1000
Cost
$100
Validity
3y

Domain Weights

AI 및 ML 기초20%
생성형 AI 기초24%
파운데이션 모델 응용28%
책임 있는 AI 지침14%
AI 솔루션의 보안·규정 준수·거버넌스14%
Format객관식·복수응답
Prerequisites없음(권장: AWS·AI/ML 6개월 노출)
Languages영어, 한국어, 일본어, 중국어 간체, 프랑스어, 독일어, 이탈리아어, 포르투갈어(브라질), 스페인어

Benefits & Tips

  • 합격하면 다음 시험 50% 할인 바우처가 생깁니다. AWS Certification 계정의 "Benefits"에서 확인하고 재인증·다른 자격증 응시에 쓸 수 있어요(만료일이 있으니 그 전에 사용).
  • 인증은 3년간 유효하며 만료 전 재인증이 필요합니다. 재인증 때도 이 50% 바우처를 쓸 수 있어요.
  • 무료 재응시는 없습니다(매 응시 전액 결제). 첫 시도에 붙는 게 가장 저렴하니, 모의고사로 합격선을 넘긴 뒤 응시하세요.
  • 합격하면 Credly 디지털 배지가 발급돼 링크드인·이메일 서명에 붙일 수 있습니다.

FAQ

AIF-C01 시험은 몇 문항이고 시험 시간은 얼마나 되나요?+

AIF-C01은 총 65문항이며, 시험 시간은 90분입니다. 문항은 객관식과 복수응답형으로 출제됩니다.

합격 점수는 몇 점인가요?+

1000점 만점에 700점 이상이면 합격입니다. 점수는 문항 난이도를 보정한 스케일 점수라 단순 정답률과는 다릅니다.

응시료는 얼마이고 어떻게 접수하나요?+

응시료는 미화 $100이며, Pearson VUE를 통해 시험 센터 또는 온라인 감독(프록터드) 시험으로 응시할 수 있습니다. 무료 재응시는 없어 매 응시마다 전액을 결제합니다.

자격증은 얼마나 유효한가요? 재인증은 어떻게 하나요?+

합격 후 3년간 유효하며, 만료 전 재인증이 필요합니다. 합격 시 다음 시험 50% 할인 바우처가 제공되어 재인증이나 다른 AWS 자격증 응시에 사용할 수 있습니다.

한국어로 응시할 수 있나요?+

네, 한국어를 포함해 총 9개 언어로 제공됩니다. 시험 등록 시 언어를 선택할 수 있습니다.

Official Exam Guide Register for Exam

Week 1

  • Day 1The Relationship and Differences Between AI, Machine Learning, and Deep Learning
  • Day 2Learning Types: Supervised, Unsupervised, and Reinforcement Learning
  • Day 3Problems Where ML Fits vs. Problems Where It Doesn't
  • Day 4Key Terms: Model, Training, Inference, Feature, Label, Overfitting
  • Day 5Week 1 Wrap-Up: AI/ML Fundamentals Review

Week 2

  • Day 1ML Lifecycle Overview: One Complete Cycle from Data to Operations
  • Day 2Data: Structured vs Unstructured, Data Splitting, and the Power of Quality
  • Day 3Model Evaluation Basics: Accuracy, Precision, Recall, and Overfitting/Underfitting
  • Day 4The Human Role in ML Development: Labeling, Feedback, and Iterative Improvement
  • Day 5Week 2 Summary: ML Lifecycle and Data at a Glance

Week 3

  • Day 1What Is Generative AI: Difference from Traditional ML, and Foundation Models
  • Day 2How LLMs Work: Tokens, Embeddings, Context Window, and Inference
  • Day 3Prompt Engineering Basics: Good Prompts, Zero/Few-shot, and Limitations
  • Day 4Limitations and Risks of Generative AI: Hallucinations, Bias, Non-determinism, and Appropriate Use Cases
  • Day 5Week 3 Comprehensive Review: Wrapping Up Generative AI Fundamentals at a Glance

Week 4

  • Day 1Amazon Bedrock: Fully Managed Service for Renting Foundation Models
  • Day 2Amazon SageMaker: A Platform for Directly Training and Deploying ML Models
  • Day 3AWS AI Services (1): Managed APIs Handling Images, Documents, Text, and Speech
  • Day 4AWS AI Services (2) + Amazon Q: Chatbots, Search, Recommendations, Forecasting, and Generative Assistant
  • Day 5Week 4 Comprehensive Review: Complete Map of AWS AI/ML Services

Week 5

  • Day 1Principles of Responsible AI: Fairness, Bias, Transparency, Explainability, Robustness, Privacy
  • Day 2AWS's Responsible AI Tools: SageMaker Clarify, Model Monitor, Bedrock Guardrails, AI Service Cards
  • Day 3AI Security: Least Privilege IAM, Data Encryption, PII Protection, PrivateLink, Shared Responsibility Model
  • Day 4Data Governance and Compliance: Data Origin·Quality, Model Governance, Audit·Logging, Legal and Ethical Considerations for Generative AI
  • Day 5Week 5 Comprehensive Review: Binding Responsible AI·Security·Governance into One

Week 6

  • Day 1Domain Review 1: AI/ML Fundamentals + Generative AI Fundamentals: Critical Summary
  • Day 2Domain Review 2: Foundation Model Applications (AWS AI Services) Critical Summary
  • Day 3Domain Review 3: Responsible AI + Security·Governance Critical Summary
  • Day 4Full Mock Exam Pace: Five Domains Comprehensive Questions
  • Day 5D-Day Wrap-Up: Exam Structure, Keyword → Service Translation Table, Frequently-Missed Traps