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CLF-C02 · FoundationalCloud Practitioner - Foundational
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DOP-C02 · ProfessionalDevOps Engineer - Professional
Linux Master 2 · 2급KAIT Linux Master - Level 2
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MLA-C01 · AssociateMachine Learning Engineer - Associate
  • Week 1
    • 1.The ML Lifecycle and the Role of the ML Engineer
    • 2.ML Problem Types and Evaluation Metric Basics
    • 3.AWS ML Stack at a Glance
    • 4.SageMaker Overview: Studio, Training/Inference, Built-in Algorithms
    • 5.Week 1 Comprehensive Review — ML Fundamentals & AWS Stack
  • Week 2
  • Week 3
  • Week 4
  • Week 5
  • Week 6
  • Week 7
  • Week 8
  • Week 9
  • Week 10
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
SQLD · 개발자SQL Developer (SQLD)

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← All certifications/MLA-C01

Machine Learning Engineer - Associate

10 weeks · 50 days · Associate

Start with Week 1

Read MLA-C01 pass reviews →

Exam Information

Intermediate · 1-2 years of relevant hands-on experience recommended
Questions
65
Duration
130min
Passing
720 / 1000
Cost
$150
Validity
3y

Domain Weights

Data Preparation for Machine Learning (ML)28%
ML Model Development26%
Deployment and Orchestration of ML Workflows22%
ML Solution Monitoring, Maintenance, and Security24%
FormatMultiple-choice and multiple-response
PrerequisitesNone (recommended: 1 year of experience with ML engineering services such as Amazon SageMaker)
LanguagesEnglish, Korean, Japanese, Simplified Chinese

Benefits & Tips

  • Passing earns you a 50% discount voucher for your next exam. Find it under "Benefits" in your AWS Certification account and use it for recertification or another certification (it has an expiry date, so use it before then).
  • The certification is valid for 3 years and must be renewed before it expires. You can use the 50% voucher for recertification too.
  • There is no free retake (every attempt is paid in full). Passing on the first try is the cheapest route, so take the real exam once your mock exam scores clear the passing mark.
  • Passing also earns you a Credly digital badge that you can add to LinkedIn and your email signature.

FAQ

How many questions are on the MLA-C01 exam, and how long is it?+

MLA-C01 has 65 questions and a time limit of 130 minutes. Questions are multiple-choice and multiple-response.

What is the passing score?+

A score of 720 or higher out of 1000 is required to pass. Scores are scaled to account for question difficulty, so they are not the same as a simple percentage of correct answers.

How much does the exam cost, and how do I register?+

The exam fee is US $150. You can take it through Pearson VUE at a test center or as an online proctored exam. There is no free retake, so you pay the full fee each time you sit the exam.

How long is the certification valid, and how do I recertify?+

The certification is valid for 3 years after you pass, and you must recertify before it expires. Passing the exam earns you a 50% discount voucher for your next exam, which you can use to recertify or to take another AWS certification.

Can I take the exam in Korean?+

Yes. The exam is offered in 4 languages, including Korean. You can choose your language when you register for the exam.

Official Exam Guide Register for Exam

Week 1

  • Day 1The ML Lifecycle and the Role of the ML Engineer
  • Day 2ML Problem Types and Evaluation Metric Basics
  • Day 3AWS ML Stack at a Glance
  • 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 Preparation
  • Day 5Week 2 Comprehensive Review — Data Collection & Storage Recap

Week 3

  • Day 1Feature Engineering: The Art of Turning Data into Numbers a Model 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 Review

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 1SageMaker Pipelines: Steps, DAG, Parameters, Condition Steps
  • Day 2Model Registry & Model Governance: Model Package Groups, Approval Workflows, Version Management
  • Day 3CI/CD for ML: SageMaker Projects, CodePipeline/CodeBuild Integration, Automated Deployment
  • Day 4IaC & Workflow Orchestration: CloudFormation/CDK, Step Functions, EventBridge, Airflow (MWAA)
  • Day 5Week 7 Synthesis: MLOps Review

Week 8

  • Day 1SageMaker Model Monitor: Data Quality and Model Quality Drift
  • Day 2Bias and Explainability Drift: Monitoring During Operations with Clarify
  • Day 3Operational Monitoring: CloudWatch Metrics/Alarms, Endpoint Latency/Errors, Logging
  • Day 4Retraining and Model Maintenance: Automated Retraining Pipelines and A/B and Shadow Testing
  • Day 5Week 8 Summary: ML Solution Monitoring and Maintenance Review

Week 9

  • Day 1SageMaker IAM Security: Execution Roles and Least Privilege
  • Day 2Network Isolation: VPC-Mode SageMaker and PrivateLink
  • Day 3Protecting Data and Models: KMS Encryption and Secrets
  • Day 4ML Cost Optimization: From Spot Training to Cost Monitoring
  • Day 5Week 9 Wrap-Up: Security, Governance, and Cost Review

Week 10

  • Day 1Domains 1 and 2 Integrated Review: Data Preparation + Model Development
  • Day 2Domains 3 & 4 Integrated Review: Deployment & Orchestration + Monitoring & Security
  • Day 3Full Mock Exam Pace: A Final Check with the 4-Domain Service Map and Decision Trees
  • Day 4Commonly Missed Traps and Keywords: A "Requirement → SageMaker Feature" Translation Table
  • Day 5D-Day Wrap-Up: Exam Structure, Time Management, and Scenario Decomposition Strategy