Cert Notes/ Commute Study Notes
Roadmap
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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
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
  • Week 1
    • 1.The ML Lifecycle (from a Specialty Perspective)
    • 2.Data Storage for ML: S3, EFS, FSx for Lustre, and Data Formats
    • 3.Data Ingestion: Kinesis, Glue, Batch vs. Streaming
    • 4.Data Labeling: SageMaker Ground Truth, Active Learning, Label Quality
    • 5.Week 1 Comprehensive Review: ML Overview & Data Engineering 1
  • Week 2
    • 1.Data Transformation and ETL: AWS Glue, Spark, and EMR
    • 2.Automating ML Training Pipelines: Step Functions and SageMaker Pipelines
    • 3.Data Augmentation and Synthesis: Addressing Insufficient and Imbalanced Data
    • 4.Data Storage and Access Optimization: Pipe vs File Mode, FSx for Lustre, Distributed Training
    • 5.Week 2 Comprehensive Review: From Transformation to Distributed Training Data Supply
  • Week 3
    • 1.Data Cleaning: Missing Values, Outlier Detection, Duplicates and Errors
    • 2.Feature Engineering: Scaling, Encoding, and Binning
    • 3.Time Series, Text Features, and High-Cardinality Categorical Handling
    • 4.SageMaker Tools: Data Wrangler, Processing Job, Feature Store
    • 5.Week 3 Comprehensive Review: Cleaning and Feature Engineering
  • Week 4
    • 1.Dimensionality Reduction: PCA, t-SNE, and the Curse of Dimensionality
    • 2.Feature Selection: Filter, Wrapper, Embedded, Importance, Multicollinearity
    • 3.Data Visualization: Distribution, Correlation, QuickSight, Insights
    • 4.Handling Class Imbalance: Over/Undersampling, SMOTE, Class Weights, Evaluation
    • 5.Week 4 Comprehensive Review: Dimensionality, Feature Selection, Visualization, Imbalance
  • Week 5
    • 1.Statistical Foundations: Distribution, Central Tendency, Dispersion, Transformations, Sample and Population
    • 2.Correlation and Relationships: Correlation Coefficients, Causation vs. Correlation, Multivariate Relationships
    • 3.Data Leakage: Causes, Detection, Prevention; Time Series Leakage; Target Leakage
    • 4.Validation Design: train/validation/test Split, Cross-Validation, Time Series Split, Stratified Sampling
    • 5.Week 5 Comprehensive Review: Statistics and Validation Design
  • Week 6
    • 1.Algorithm Selection: Problem Type to Mapping
    • 2.SageMaker Builtin 1: XGBoost, Linear Learner, K-Means, KNN
    • 3.SageMaker Builtin 2: Text, Image, Time Series, Recommendation
    • 4.Unsupervised/Anomaly Detection: RCF, PCA, IP Insights, Topic Models (LDA/NTM)
    • 5.Week 6 Comprehensive Review: Algorithm Selection and SageMaker Builtins
  • Week 7
    • 1.Neural Network Foundations: Perceptron to Backpropagation
    • 2.CNN: Convolutional Neural Networks and Computer Vision
    • 3.RNNs and Sequences: From LSTM to Transformer
    • 4.Learning Techniques and Transfer Learning
    • 5.Week 7 Synthesis: Deep Learning Summary
  • Week 8
    • 1.SageMaker Training Jobs: Estimator, Input Modes, Distributed Learning, Spot
    • 2.Hyperparameter Tuning (AMT): Bayesian, Random, Hyperband
    • 3.Overfitting/Underfitting: Diagnosis and Regularization/Data Augmentation
    • 4.Learning Optimization: Batch Size, Learning Rate, Gradient Issues, Debugger/Profiler
    • 5.Week 8 Review: Training, Tuning, Generalization
  • Week 9
    • 1.Classification Evaluation Metrics: Accuracy, Precision, Recall, F1 and Confusion Matrix
    • 2.ROC/AUC and Threshold Adjustment: Reading Model Performance with Curves
    • 3.Regression Evaluation Metrics: RMSE, MAE, MAPE, R² and Residual Analysis
    • 4.Model Debugging and Bias: SageMaker Debugger and Clarify
    • 5.Week 9 Review: Evaluation and Debugging
  • Week 10
    • 1.Inference Options: Real-time vs Serverless vs Asynchronous vs Batch Transform
    • 2.Real-time Endpoint Operations: Configuration, Auto Scaling, Multi-Model
    • 3.Inference Optimization: Neo, Elastic Inference, Inferentia, Inference Pipelines
    • 4.Deployment Strategies: A/B Testing, Blue/Green, Canary, Shadow, Rollback
    • 5.Week 10 Review: ML Implementation & Operations 1 — Deployment & Inference
  • Week 11
    • 1.Model Monitoring: SageMaker Model Monitor and Drift Response
    • 2.MLOps: SageMaker Pipelines, Model Registry, CI/CD
    • 3.ML Security: IAM Execution Roles, VPC Isolation, KMS Encryption
    • 4.Operations & Cost: Cost Optimization, Logging/Audit, Disaster Recovery
    • 5.Week 11 Review: Monitoring, MLOps, Security, Operations
  • Week 12
    • 1.Domains 1 & 2 Integration: Data Engineering + EDA
    • 2.Domain 3 Integration: Modeling (Algorithm to Evaluation)
    • 3.Domain 4 Integration: ML Implementation & Operations
    • 4.Synthesis: 4 Domains + End-to-End Scenarios
    • 5.D-Day Wrap-Up: Exam Structure, Time Allocation, Requirement Translation Tables, Trap Roundup
← All certifications/MLS-C01

Machine Learning - Specialty

12 weeks · 60 days · Specialty

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

전문 · 해당 도메인 실무 경험 권장
Questions
65
Duration
180min
Passing
750 / 1000
Cost
$300
Validity
3y

Domain Weights

데이터 엔지니어링20%
탐색적 데이터 분석24%
모델링36%
머신러닝 구현 및 운영20%
Format객관식·복수응답
Prerequisites없음(권장: ML/딥러닝 개발·운영 2년 이상 경험)
Languages영어, 한국어, 일본어, 중국어 간체

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

MLS-C01 시험은 몇 문항이고 시험 시간은 얼마나 되나요?+

MLS-C01은 총 65문항이며, 시험 시간은 180분입니다. 문항은 객관식과 복수응답형으로 출제됩니다.

합격 점수는 몇 점인가요?+

1000점 만점에 750점 이상이면 합격입니다. 점수는 문항 난이도를 보정한 스케일 점수라 단순 정답률과는 다릅니다.

응시료는 얼마이고 어떻게 접수하나요?+

응시료는 미화 $300이며, 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 4개 언어로 제공됩니다. 시험 등록 시 언어를 선택할 수 있습니다.

Official Exam Guide Register for Exam

Week 1

  • Day 1The ML Lifecycle (from a Specialty Perspective)
  • Day 2Data Storage for ML: S3, EFS, FSx for Lustre, and Data Formats
  • Day 3Data Ingestion: Kinesis, Glue, Batch vs. Streaming
  • Day 4Data Labeling: SageMaker Ground Truth, Active Learning, Label Quality
  • Day 5Week 1 Comprehensive Review: ML Overview & Data Engineering 1

Week 2

  • Day 1Data Transformation and ETL: AWS Glue, Spark, and EMR
  • Day 2Automating ML Training Pipelines: Step Functions and SageMaker Pipelines
  • Day 3Data Augmentation and Synthesis: Addressing Insufficient and Imbalanced Data
  • Day 4Data Storage and Access Optimization: Pipe vs File Mode, FSx for Lustre, Distributed Training
  • Day 5Week 2 Comprehensive Review: From Transformation to Distributed Training Data Supply

Week 3

  • Day 1Data Cleaning: Missing Values, Outlier Detection, Duplicates and Errors
  • Day 2Feature Engineering: Scaling, Encoding, and Binning
  • Day 3Time Series, Text Features, and High-Cardinality Categorical Handling
  • Day 4SageMaker Tools: Data Wrangler, Processing Job, Feature Store
  • Day 5Week 3 Comprehensive Review: Cleaning and Feature Engineering

Week 4

  • Day 1Dimensionality Reduction: PCA, t-SNE, and the Curse of Dimensionality
  • Day 2Feature Selection: Filter, Wrapper, Embedded, Importance, Multicollinearity
  • Day 3Data Visualization: Distribution, Correlation, QuickSight, Insights
  • Day 4Handling Class Imbalance: Over/Undersampling, SMOTE, Class Weights, Evaluation
  • Day 5Week 4 Comprehensive Review: Dimensionality, Feature Selection, Visualization, Imbalance

Week 5

  • Day 1Statistical Foundations: Distribution, Central Tendency, Dispersion, Transformations, Sample and Population
  • Day 2Correlation and Relationships: Correlation Coefficients, Causation vs. Correlation, Multivariate Relationships
  • Day 3Data Leakage: Causes, Detection, Prevention; Time Series Leakage; Target Leakage
  • Day 4Validation Design: train/validation/test Split, Cross-Validation, Time Series Split, Stratified Sampling
  • Day 5Week 5 Comprehensive Review: Statistics and Validation Design

Week 6

  • Day 1Algorithm Selection: Problem Type to Mapping
  • Day 2SageMaker Builtin 1: XGBoost, Linear Learner, K-Means, KNN
  • Day 3SageMaker Builtin 2: Text, Image, Time Series, Recommendation
  • Day 4Unsupervised/Anomaly Detection: RCF, PCA, IP Insights, Topic Models (LDA/NTM)
  • Day 5Week 6 Comprehensive Review: Algorithm Selection and SageMaker Builtins

Week 7

  • Day 1Neural Network Foundations: Perceptron to Backpropagation
  • Day 2CNN: Convolutional Neural Networks and Computer Vision
  • Day 3RNNs and Sequences: From LSTM to Transformer
  • Day 4Learning Techniques and Transfer Learning
  • Day 5Week 7 Synthesis: Deep Learning Summary

Week 8

  • Day 1SageMaker Training Jobs: Estimator, Input Modes, Distributed Learning, Spot
  • Day 2Hyperparameter Tuning (AMT): Bayesian, Random, Hyperband
  • Day 3Overfitting/Underfitting: Diagnosis and Regularization/Data Augmentation
  • Day 4Learning Optimization: Batch Size, Learning Rate, Gradient Issues, Debugger/Profiler
  • Day 5Week 8 Review: Training, Tuning, Generalization

Week 9

  • Day 1Classification Evaluation Metrics: Accuracy, Precision, Recall, F1 and Confusion Matrix
  • Day 2ROC/AUC and Threshold Adjustment: Reading Model Performance with Curves
  • Day 3Regression Evaluation Metrics: RMSE, MAE, MAPE, R² and Residual Analysis
  • Day 4Model Debugging and Bias: SageMaker Debugger and Clarify
  • Day 5Week 9 Review: Evaluation and Debugging

Week 10

  • Day 1Inference Options: Real-time vs Serverless vs Asynchronous vs Batch Transform
  • Day 2Real-time Endpoint Operations: Configuration, Auto Scaling, Multi-Model
  • Day 3Inference Optimization: Neo, Elastic Inference, Inferentia, Inference Pipelines
  • Day 4Deployment Strategies: A/B Testing, Blue/Green, Canary, Shadow, Rollback
  • Day 5Week 10 Review: ML Implementation & Operations 1 — Deployment & Inference

Week 11

  • Day 1Model Monitoring: SageMaker Model Monitor and Drift Response
  • Day 2MLOps: SageMaker Pipelines, Model Registry, CI/CD
  • Day 3ML Security: IAM Execution Roles, VPC Isolation, KMS Encryption
  • Day 4Operations & Cost: Cost Optimization, Logging/Audit, Disaster Recovery
  • Day 5Week 11 Review: Monitoring, MLOps, Security, Operations

Week 12

  • Day 1Domains 1 & 2 Integration: Data Engineering + EDA
  • Day 2Domain 3 Integration: Modeling (Algorithm to Evaluation)
  • Day 3Domain 4 Integration: ML Implementation & Operations
  • Day 4Synthesis: 4 Domains + End-to-End Scenarios
  • Day 5D-Day Wrap-Up: Exam Structure, Time Allocation, Requirement Translation Tables, Trap Roundup