AWS Certified Machine Learning Engineer - Associate : MLA-C01
About the Certification
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification validates your ability to build, deploy, and optimise machine learning (ML) solutions using AWS services. It focuses on data preparation, feature engineering, model training, deployment, monitoring, and MLOps practices.
If you are aiming for an AWS certification that proves your practical ML engineering skills, this is one of the most valuable and in-demand credentials globally.
Table of Contents
Exam Overview
Exam Name | AWS Certified Machine Learning Engineer – Associate |
Exam Code | MLA-C01 |
Format | Multiple-choice & multiple-response |
Duration | 170 minutes |
Exam Cost | USD 150 (plus regional taxes) |
Delivery Method | Online proctored or in-person at Pearson VUE test centers |
Validity | 3 years from the date of certification |
Key Domains & Weighting
Domain 1: Data Engineering – 20%
Domain 2: Exploratory Data Analysis – 24%
Domain 3: Modeling – 36%
Domain 4: Machine Learning Operations (MLOps) – 20%
Who Should Take This Exam
This certification is ideal for:
- Machine Learning Engineers
- Data Scientists and Data Engineers
- AI Engineers and ML Developers
- Software Engineers working with ML pipelines
- Professionals looking to enhance their ML careers with an AWS certification
- Anyone preparing for real-world ML engineering roles
What You'll Learn
Data Engineering & Preparation
- Data ingestion using Kinesis, Glue, and S3
- Feature engineering workflows
- Preprocessing and cleaning datasets at scale
Model Training & Optimization
- Building ML models using Amazon SageMaker
- Hyperparameter tuning, model evaluation, and debugging
- Selecting the right algorithm based on business needs
Deployment & MLOps Best Practices
- Deploying models using SageMaker endpoints
- Monitoring, logging, and model drift detection
- Automating ML pipelines with SageMaker Pipelines, Lambda & Step Functions
Preparation Tips
- Gain hands-on experience with Amazon SageMaker, Glue, Lambda & ML Pipelines
- Study AWS whitepapers and recommended documentation
- Practice end-to-end ML projects using AWS Free Tier
- Take structured AWS practice exams to understand the exam pattern
- Use Certify360’s adaptive learning system to strengthen weak areas
- Explore real-world ML use cases and deployment scenarios
Why Choose Certify360
- Demonstrates expertise in ML model building, optimization, and deployment
- Opens opportunities in ML engineering, AI engineering, and cloud ML roles
- Strengthens your portfolio with real-world AWS ML projects
- Enhances credibility in the AI/ML job market
- Builds a strong foundation for advanced AWS and machine learning certifications
- Increases earning potential and job stability in the rapidly growing AI field
Related Certifications
- SAA-C03: AWS Certified Solution Architect-Associate
- AIF-C01: AWS Certified AI Practitioner
- MLA-C01: AWS Certified Machine Learning Engineer – Associate
- DOP-C02: AWS Certified DevOps Engineer – Professional
- CLF-C02: AWS Certified Cloud Practitioner
- SAP-C02: AWS Certified Solutions Architect – Professional
FAQs
1. Do I need ML experience before taking MLA-C01?
Basic understanding of ML concepts is recommended, but hands-on training with Certify360 can bridge the gap.
Does this certification focus only on SageMaker?
No. While SageMaker is central, the exam covers data engineering, MLOps, analytics, and model deployment across multiple AWS services.
3. Is MLA-C01 difficult?
It is considered intermediate-level, but structured preparation and AWS practice exams make it achievable.
4. Will this certification help me get a job?
Yes. ML engineering is one of the fastest-growing cloud job roles, and this certification boosts your credibility.
Start Your AWS Journey Today
Prepare for your AWS Certified Machine Learning Engineer – Associate: MLA-C01
exam with Certify360 and gain the skills, confidence, and certification you need to advance your cloud career.
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