1. Exam Overview
The AWS Certified Machine Learning – Specialty (MLS-C01) exam validates your ability to build, train, tune, and deploy machine learning models on AWS.
It’s designed for data scientists and ML engineers with hands-on experience in designing and running ML workloads in the AWS cloud.
📘 Official Exam Guide (PDF): AWS Certified Machine Learning – Specialty Exam Guide
🧩 Practice Questions: Cloud Pass MLS-C01 Practice Page
Exam Details
- Format: Multiple-choice / Multiple-response
- Duration: 180 minutes
- Recommended Experience: 2+ years designing or implementing ML solutions on AWS
- Core Areas: Data Engineering, EDA, Modeling, ML Implementation & Operations
2. Exam Domains
| Domain | Description | Weight |
|---|---|---|
| Domain 1 | Data Engineering | ~20% |
| Domain 2 | Exploratory Data Analysis (EDA) | ~24% |
| Domain 3 | Modeling | ~36% |
| Domain 4 | ML Implementation & Operations | ~20% |
The exam emphasizes practical understanding — not just algorithms, but how to map business problems to ML solutions using AWS services.
3. Study Strategy
(1) Understand the End-to-End ML Lifecycle
Review the flow from data ingestion to model deployment:
- Collect → Clean → Analyze → Train → Deploy → Monitor
Understand when to use batch vs real-time pipelines and how to optimize costs and latency.
(2) Master Core AWS Services
- Data Engineering: S3, Glue, Redshift, Kinesis
- Modeling & Training: SageMaker (built-in algorithms, AutoPilot, hyperparameter tuning)
- Monitoring: CloudWatch, SageMaker Model Monitor
- Deployment: SageMaker Endpoints, Elastic Inference, ECS for model serving
(3) Practice Problem Solving
During practice, focus on why a specific AWS service or architecture is chosen:
- “Which approach best handles imbalanced data?”
- “How do I automate retraining for model drift?”
👉 Cloud Pass MLS-C01 Practice Page
(4) Use AWS Whitepapers & Documentation
- Machine Learning Lens – AWS Well-Architected Framework
- Amazon SageMaker Best Practices
- Data Analytics on AWS
4. Key AWS Services Summary
| Area | Services | Key Concepts |
|---|---|---|
| Data Engineering | S3, Glue, Redshift | Data pipelines, schema design, batch vs stream |
| EDA | Athena, QuickSight, Pandas, SageMaker Studio | Outlier detection, feature selection |
| Modeling | SageMaker, AutoPilot, TensorFlow, PyTorch | Algorithm selection, tuning, metrics |
| Operations | SageMaker Endpoints, CloudWatch, Model Monitor | A/B testing, scaling, retraining |
5. Common Exam Scenarios
- Choosing the right ML algorithm for classification vs regression
- Designing feature engineering workflows using SageMaker Processing
- Handling imbalanced datasets with SMOTE or class weighting
- Monitoring deployed models for drift and triggering retraining
- Building a recommendation system with Amazon Personalize
6. Recommended Study Roadmap
| Week | Goal | Focus |
|---|---|---|
| Week 1 | Understand ML workflow and exam scope | Review official guide, AWS services overview |
| Week 2 | Data Engineering & EDA | Data collection, cleaning, visualization |
| Week 3 | Modeling Deep Dive | Algorithm practice, SageMaker tuning |
| Week 4 | Deployment & MLOps | Endpoint deployment, monitoring, retraining |
| Week 5 | Practice Exams | Cloud Pass tests, time management, weak areas |
7. Final Tips
- Learn to explain why you’d choose one AWS service over another.
- Focus on practical workflows and cost-efficient ML architectures.
- Take mock exams regularly to identify weak areas.
- Manage time wisely — some questions are long and scenario-based.
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to help you master AWS Machine Learning concepts with real-world examples.