Cert Notes/ Commute Study Notes
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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
  • Week 1
    • 1.ML Lifecycle and the Role of ML Engineers
    • 2.ML Problem Types and Evaluation Metrics Basics
    • 3.Day 3
    • 4.SageMaker Overview: Studio, Training/Inference, Built-in Algorithms
    • 5.Week 1 Comprehensive Review — ML Fundamentals & AWS Stack
  • Week 2
    • 1.Data Collection: S3 Data Lake, Kinesis, Batch Ingestion, Data Formats
    • 2.Data Catalog & ETL: AWS Glue and DataBrew
    • 3.Query & Exploration: Athena, Redshift, EDA Basics
    • 4.Data Storage Strategy: Partitioning, Format Optimization, Training Readiness
    • 5.Week 2 Comprehensive Review — Data Collection & Storage Recap
  • Week 3
    • 1.Feature Engineering: The Art of Transforming Data into Numbers Models Can Read
    • 2.SageMaker Data Wrangler: No-Code Data Preparation
    • 3.SageMaker Feature Store: Managing Features as Assets
    • 4.Data Bias·Quality: Clarify, Class Imbalance Handling, Data Split
    • 5.Week 3 Comprehensive Review — Feature Engineering·Data Quality
  • Week 4
    • 1.SageMaker Training Job: Estimator, Input Channels, Instances, Spot
    • 2.Built-in Algorithms: XGBoost, Linear Learner, Image·Text, Input Formats
    • 3.Hyperparameter Tuning (AMT): Bayesian·Random·Grid, Early Stopping, Warm Start
    • 4.JumpStart·Pre-trained Models·Transfer Learning + Training Cost Optimization
    • 5.Week 4 Comprehensive Review: Model Development 1 — SageMaker Training
  • Week 5
    • 1.Custom Training: Script Mode, BYOC, Framework Containers
    • 2.Distributed Training: Data Parallel and Model Parallel
    • 3.Debugging and Profiling: SageMaker Debugger and Profiler
    • 4.Model Evaluation: Metric Selection, Overfitting, Cross-validation, Confusion Matrix
    • 5.Week 5 Comprehensive: Model Development 2 Review
  • Week 6
    • 1.Inference Options Overview: 4 Deployment Modes and Selection Criteria
    • 2.Real-time Endpoints: Configuration, Auto-scaling, Instance Selection
    • 3.Cost & Advanced Inference: Multi-model, Multi-container, Inference Pipeline, Inferentia
    • 4.Batch & Serverless Inference Deep Dive: Throughput Tuning & Cost Tradeoffs
    • 5.Week 6 Synthesis: Inference Deployment Review
  • Week 7
    • 1.Declare parameters, injectible at execution
    • 2.Create package group bundling models of same purpose
    • 3.buildspec.yml — commands CodeBuild executes
    • 4.CloudFormation: SageMaker endpoint declared as code
    • 5.Synthesis code: pipeline end with condition passes → approval triggers deploy
  • Week 8
    • 1.SageMaker Model Monitor: Data Quality and Model Quality Drift
    • 2.Bias and Explainability Drift: Monitoring During Operations with Clarify
    • 3.Filter recent errors from log group
    • 4.Day 4
    • 5.Best practice: aggregating operational metrics + model monitor metrics on one dashboard
  • Week 9
    • 1.Day 1
    • 2.Day 2
    • 3.Data and Model Protection: KMS Encryption and Secrets
    • 4.Day 4
    • 5.Day 5
  • Week 10
    • 1.Day 1
    • 2.Day 2
    • 3.Day 3
    • 4.Day 4
    • 5.Day 5
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
← All certifications/MLA-C01

Machine Learning Engineer - Associate

10 weeks · 50 days · Associate

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

Read MLA-C01 pass reviews →

Exam Information

중급 · 관련 실무 1~2년 권장
Questions
65
Duration
130min
Passing
720 / 1000
Cost
$150
Validity
3y

Domain Weights

ML을 위한 데이터 준비28%
ML 모델 개발26%
ML 워크플로 배포 및 오케스트레이션22%
ML 솔루션 모니터링·유지보수·보안24%
Format객관식·복수응답
Prerequisites없음(권장: SageMaker 등 ML 엔지니어링 서비스 1년 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

MLA-C01 시험은 몇 문항이고 시험 시간은 얼마나 되나요?+

MLA-C01은 총 65문항이며, 시험 시간은 130분입니다. 문항은 객관식과 복수응답형으로 출제됩니다.

합격 점수는 몇 점인가요?+

1000점 만점에 720점 이상이면 합격입니다. 점수는 문항 난이도를 보정한 스케일 점수라 단순 정답률과는 다릅니다.

응시료는 얼마이고 어떻게 접수하나요?+

응시료는 미화 $150이며, 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 1ML Lifecycle and the Role of ML Engineers
  • Day 2ML Problem Types and Evaluation Metrics Basics
  • Day 3Day 3
  • Day 4SageMaker Overview: Studio, Training/Inference, Built-in Algorithms
  • Day 5Week 1 Comprehensive Review — ML Fundamentals & AWS Stack

Week 2

  • Day 1Data Collection: S3 Data Lake, Kinesis, Batch Ingestion, Data Formats
  • Day 2Data Catalog & ETL: AWS Glue and DataBrew
  • Day 3Query & Exploration: Athena, Redshift, EDA Basics
  • Day 4Data Storage Strategy: Partitioning, Format Optimization, Training Readiness
  • Day 5Week 2 Comprehensive Review — Data Collection & Storage Recap

Week 3

  • Day 1Feature Engineering: The Art of Transforming Data into Numbers Models Can Read
  • Day 2SageMaker Data Wrangler: No-Code Data Preparation
  • Day 3SageMaker Feature Store: Managing Features as Assets
  • Day 4Data Bias·Quality: Clarify, Class Imbalance Handling, Data Split
  • Day 5Week 3 Comprehensive Review — Feature Engineering·Data Quality

Week 4

  • Day 1SageMaker Training Job: Estimator, Input Channels, Instances, Spot
  • Day 2Built-in Algorithms: XGBoost, Linear Learner, Image·Text, Input Formats
  • Day 3Hyperparameter Tuning (AMT): Bayesian·Random·Grid, Early Stopping, Warm Start
  • Day 4JumpStart·Pre-trained Models·Transfer Learning + Training Cost Optimization
  • Day 5Week 4 Comprehensive Review: Model Development 1 — SageMaker Training

Week 5

  • Day 1Custom Training: Script Mode, BYOC, Framework Containers
  • Day 2Distributed Training: Data Parallel and Model Parallel
  • Day 3Debugging and Profiling: SageMaker Debugger and Profiler
  • Day 4Model Evaluation: Metric Selection, Overfitting, Cross-validation, Confusion Matrix
  • Day 5Week 5 Comprehensive: Model Development 2 Review

Week 6

  • Day 1Inference Options Overview: 4 Deployment Modes and Selection Criteria
  • Day 2Real-time Endpoints: Configuration, Auto-scaling, Instance Selection
  • Day 3Cost & Advanced Inference: Multi-model, Multi-container, Inference Pipeline, Inferentia
  • Day 4Batch & Serverless Inference Deep Dive: Throughput Tuning & Cost Tradeoffs
  • Day 5Week 6 Synthesis: Inference Deployment Review

Week 7

  • Day 1Declare parameters, injectible at execution
  • Day 2Create package group bundling models of same purpose
  • Day 3buildspec.yml — commands CodeBuild executes
  • Day 4CloudFormation: SageMaker endpoint declared as code
  • Day 5Synthesis code: pipeline end with condition passes → approval triggers deploy

Week 8

  • Day 1SageMaker Model Monitor: Data Quality and Model Quality Drift
  • Day 2Bias and Explainability Drift: Monitoring During Operations with Clarify
  • Day 3Filter recent errors from log group
  • Day 4Day 4
  • Day 5Best practice: aggregating operational metrics + model monitor metrics on one dashboard

Week 9

  • Day 1Day 1
  • Day 2Day 2
  • Day 3Data and Model Protection: KMS Encryption and Secrets
  • Day 4Day 4
  • Day 5Day 5

Week 10

  • Day 1Day 1
  • Day 2Day 2
  • Day 3Day 3
  • Day 4Day 4
  • Day 5Day 5