AWS Machine Learning Certification Study Guide & Exam Syllabus

Q: What does the AWS Machine Learning certification syllabus cover?

The AWS Machine Learning exam syllabus covers multiple weighted domains. Each domain is weighted differently, so focus your training on higher-weighted domains first. Review the complete domain breakdown for objectives and key concepts.

Exam Syllabus & Domains

The AWS Machine Learning certification exam covers the following domains. Focus your training time proportionally to each domain's weight.

Domain 1 20%

Data Engineering

1.1 Create data repositories for machine learning
1.2 Identify and implement data ingestion solutions
1.3 Identify and implement data transformation solutions

S3 Data Lakes and Lake FormationKinesis Data Streams vs FirehoseAWS Glue ETL Jobs and CrawlersGlue Data CatalogEMR with Spark for Large-Scale ProcessingAthena for Ad-Hoc QueryingData Pipeline OrchestrationBatch vs Real-Time IngestionData Partitioning StrategiesParquet and ORC File Formats

Domain 2 24%

Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling
2.2 Perform feature engineering
2.3 Analyze and visualize data for machine learning
2.4 Select appropriate statistical methods for data analysis

Handling Missing Data (Imputation Strategies)Outlier Detection and TreatmentOne-Hot Encoding vs Label EncodingNormalization vs StandardizationPrincipal Component Analysis (PCA)t-SNE for VisualizationCorrelation AnalysisClass Imbalance (SMOTE, Oversampling)SageMaker Data WranglerGround Truth Labeling

Domain 3 36%

Modeling

3.1 Frame business problems as machine learning problems
3.2 Select the appropriate model for a given problem
3.3 Train machine learning models
3.4 Perform hyperparameter optimization
3.5 Evaluate machine learning models

SageMaker Built-In Algorithms (XGBoost, Linear Learner, KNN)Image Classification and Object DetectionSequence-to-Sequence (Seq2Seq) ModelsBlazingText and Word2VecRandom Cut Forest (Anomaly Detection)Hyperparameter Tuning JobsConfusion Matrix, Precision, Recall, F1AUC-ROC CurvesBias-Variance TradeoffRegularization (L1/L2)

Domain 4 20%

Machine Learning Implementation and Operations

4.1 Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
4.2 Recommend and implement appropriate ML services and features for a given problem
4.3 Apply security practices to ML solutions

SageMaker Real-Time EndpointsBatch Transform JobsMulti-Model EndpointsSageMaker Model Monitor (Data Drift)SageMaker Pipelines (MLOps)Model Registry and VersioningA/B Testing with Production VariantsAuto-Scaling Inference EndpointsSageMaker Neo (Model Compilation)Inference Recommender

Where to Focus Your Study Time

Domains with higher weight have more exam questions — allocate your study hours accordingly.

D1 Data Engineering

20%

D2 Exploratory Data Analysis

24%

D3 Modeling

36%

D4 Machine Learning Implementation and Operations

20%

Study Tips

Create a 14-week study schedule and assign specific domains to each week
Focus more time on higher-weighted domains — they have more exam questions
Use practice quizzes to identify weak areas early, then revisit those domains
Study in focused 25-minute blocks (Pomodoro technique) with 5-minute breaks
Create flashcards for key terms, acronyms, and port numbers
Review domain objectives weekly to track your progress and adjust your plan

Free Study Resources

📋

Study Roadmap

Week-by-week study plan with free resources

✅

Study Tracker

Track objective completion with progress dashboard

💰

Cost Calculator

Total cost breakdown and ROI analysis

🧪

Practice Quiz

Test your knowledge with free practice questions

Practice Quiz

Test your knowledge before the exam with our free practice quiz.

Take the AWS Machine Learning Practice Quiz

Get the AWS Machine Learning Study Planner

Fillable PDF with 14-week schedule, domain trackers, flashcard templates, progress tracking, and quick reference sheets. Available in Standard, ADHD-Friendly, Dark Mode, and 4-Format Bundle.

Get the Study Planner — $5.99

Also available as a 4-Format Bundle for $15.99

Free Training Resources

Use these free tools to support your AWS Machine Learning certification training:

Cybersecurity Practice Tests — 3,150+ free questions across 66 certifications
Study Roadmap — Structured learning path for AWS Machine Learning
Study Progress Tracker — Track hours and domain coverage
CVSS Calculator — Practice scoring vulnerabilities

Frequently Asked Questions

What is the AWS Machine Learning certification?

The AWS Machine Learning (MLS-C01) is a professional IT certification that validates your knowledge and skills in the exam domains covered. It is recognized globally by employers and is a valuable credential for career advancement in cybersecurity and IT.

What does the AWS Machine Learning certification syllabus cover?

The AWS Machine Learning exam syllabus covers 4 domains. Each domain is weighted differently, so focus your training on higher-weighted domains first. Review the complete domain breakdown above for objectives and key concepts.

How should I study for AWS Machine Learning?

Create a structured study plan covering all exam domains, use practice tests to identify weak areas, and review key concepts regularly. A fillable study planner can help you organize your training with weekly schedules and progress tracking.

How long does it take to prepare for AWS Machine Learning?

Preparation time varies by experience level. Most candidates spend 8-12 weeks of dedicated training. Using a structured study planner with domain-by-domain breakdown helps ensure you cover all certification objectives efficiently.