1. Exam Overview
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam validates your ability to design, build, deploy, and manage ML solutions on AWS.
It targets ML practitioners and engineers with at least one year of hands-on experience using SageMaker and other AWS ML services.
📘 Official Exam Guide (PDF): AWS Certified Machine Learning Engineer – Associate Exam Guide
🌐 Official Certification Page: AWS Certification – Machine Learning Engineer Associate
🧩 Practice Questions: Cloud Pass MLA-C01 Practice Page
Exam Details
- Format: 65 multiple-choice/multiple-response questions
- Duration: 130 minutes
- Experience: 1+ year working with SageMaker and AWS ML tools
- Focus: Data preparation, model development, workflow orchestration, and monitoring
2. Exam Domains
| Domain | Description | Weight |
|---|---|---|
| Domain 1 | Data Preparation for ML | 28% |
| Domain 2 | ML Model Development | 26% |
| Domain 3 | Deployment & Orchestration of ML Workflows | 22% |
| Domain 4 | Monitoring, Maintenance & Security | 24% |
This exam focuses on your ability to select and implement the right AWS services for each stage of the ML lifecycle, not just theory.
3. Study Strategy
(1) Understand the Full ML Lifecycle
Learn the end-to-end workflow:
Data Collection → Preprocessing → Modeling → Deployment → Monitoring & Optimization
(2) Master Core AWS Services
- Data Prep: S3, Glue, Redshift
- Model Development: SageMaker training, tuning, and built-in algorithms
- Deployment: SageMaker Endpoints, CI/CD, automation with Pipelines
- Monitoring & Security: CloudWatch, Model Monitor, IAM, KMS
(3) Practice Problem Solving
As you study, focus on why certain services or designs are optimal.
Ask yourself:
- “Which architecture meets both cost and performance goals?”
- “How can I automate retraining based on data drift?”
👉 Cloud Pass MLA-C01 Practice Page
(4) Use Official Documentation and Whitepapers
Combine the AWS exam guide with ML whitepapers and SageMaker best practices to build practical understanding.
4. Key AWS Services Summary
| Area | Services | Key Concepts |
|---|---|---|
| Data Prep | S3, Glue, Redshift | ETL, feature engineering, batch vs real-time |
| Model Development | SageMaker, tuning jobs, built-in algorithms | hyperparameter tuning, model evaluation |
| Deployment | Endpoints, Pipelines, CI/CD | scaling, version control, automation |
| Monitoring & Security | CloudWatch, Model Monitor, IAM, KMS | drift detection, auditing, access control |
5. Common Exam Scenarios
- Designing an automated ML workflow using SageMaker Pipelines
- Setting up retraining triggers for model drift detection
- Choosing the right deployment strategy for hybrid environments
- Applying IAM and KMS to secure ML workflows
6. Study Roadmap
| Week | Goal | Focus |
|---|---|---|
| Week 1 | Understand exam structure and domains | Read the official guide |
| Week 2 | Data Prep | Feature engineering and dataset management |
| Week 3 | Model Development | SageMaker training, evaluation, tuning |
| Week 4 | Deployment & Ops | CI/CD setup, endpoint optimization |
| Week 5 | Practice Tests | Cloud Pass mock exams, time management |
7. Final Tips
- Always understand the reasoning behind architecture and service choices.
- Practice hands-on with SageMaker and CloudWatch.
- Review your mistakes and focus on weak areas.
- Manage time wisely on the exam — questions are often scenario-based.
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