عراب ال IT 
امتحانات دوليه
(MLS-C01)
AWS Certified Machine Learning – Specialty
About the Certification
This is an advanced AWS certification that validates an engineer’s or data analyst’s ability to build, train, deploy, and optimize Machine Learning (ML) models on the AWS platform. It focuses on the full ML lifecycle, including data collection, preprocessing, algorithm selection, evaluation, deployment, and monitoring in production environments. The certification covers hands-on use of AWS services such as Amazon SageMaker, S3, Lambda, Glue, EMR, Kinesis, and DynamoDB, and represents the AWS Certified Machine Learning – Specialty credential for professionals building and operating large-scale AI/ML solutions on AWS.
Main Curriculum
The curriculum is divided into four main sections
- Data Engineering
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- Collecting data from multiple sources
- Data cleaning and preprocessing
- Feature engineering
- Managing data pipelines
- Exploratory Data Analysis
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- Exploratory data analysis (EDA)
- Selecting relevant features
- Handling imbalanced datasets
- Understanding patterns and trends
- Modeling
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- Selecting appropriate algorithms
- Training models using Amazon SageMaker
- Hyperparameter tuning
- Evaluating model performance
- Machine Learning Implementation and Operations
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- Model deployment (inference)
- MLOps and performance monitoring
- Retraining and continuous improvement
- Cost management and scalability
Exam Details
- Exam code: MLS-C01
- Duration: 180 minutes
- Number of questions: 65
- Question types: Multiple-choice and multiple-response
- Language: English
- Passing score: 750 out of 1000
- Exam fee: USD 300 (may vary by country and may include taxes)
- Certification validity: 3 years
Do I need certifications before it ?
No, not at all.
However, it is strongly recommended to have solid Python skills, a good understanding of statistics and machine learning algorithms, and hands-on AWS experience at least at the Associate level.
Direct experience working with Amazon SageMaker is highly beneficial.
Which certifications are recommended after this one ?
Not mandatory. However, for professionals seeking a strong path in designing and building ML solutions on AWS especially in data preparation, model training, optimization, and large-scale deployment the AWS Certified Machine Learning – Specialty certification is the strongest specialization.
What are the main career uses of the AWS Certified Machine Learning – Specialty ?
- Advanced ML / Deep Learning for deeper expertise in advanced models (DL, NLP, Computer Vision).
- Generative AI (LLMs / Fine-Tuning / RAG) for specializing in modern enterprise-grade GenAI solutions.
- MLOps (Pipelines / CI/CD / Monitoring) for operating, managing, and monitoring ML models in production.
- Data Engineering (Glue / Redshift / Lakehouse) to strengthen the data layer supporting ML models.
- AI / ML Architecture for designing enterprise-scale AI solutions.
- Cloud Security & Governance (AI-focused) for specializing in model security, privacy, and compliance.
Your Step Toward Machine Learning Mastery :
The MLS-C01 certification is one of the fastest ways to prove your ability to build and operate enterprise-grade AI solutions. It strengthens your skills in Machine Learning, MLOps, and advanced AWS services, while opening doors to high-impact roles in data engineering and AI within large organizations.
Who is this certification suitable for ?
- ML Engineers and Data Scientists.
- Professionals building ML models and deploying them to production.
- Engineers using Amazon SageMaker for training and inference.
- Those working with Big Data and ETL pipelines.
- Professionals with a strong foundation in statistics and algorithms.
- Anyone aiming for a professional career in AI/ML Cloud Engineering.