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AWS Certified Machine Learning Specialty Quick Facts
The AWS Certified Machine Learning Specialty certification empowers you to showcase in-demand expertise for building, training, and deploying machine learning solutions on AWS. This overview provides everything you need to feel confident and well-prepared, giving you a clear path to success and growth in the world of cloud-powered AI.
Why pursue the AWS Certified Machine Learning Specialty certification?
This certification validates advanced, hands-on knowledge in the full machine learning lifecycle on AWS. It demonstrates your ability to design data pipelines, explore and prepare data, select models, optimize performance, and securely operationalize ML solutions. Whether you are a data scientist, ML engineer, or architect, this certification highlights your ability to leverage services like SageMaker, Glue, and Kinesis, while also showing mastery of ML strategies including supervised learning, deep learning, and hyperparameter tuning. It proves to peers and employers that you have the expertise to turn data into intelligent, production-ready solutions.
Who should take the AWS Certified Machine Learning – Specialty certification?
The AWS Certified Machine Learning Specialty certification is designed for professionals who are passionate about data, machine learning, and AI-powered solutions in the cloud. This certification is ideal for:
- Data Scientists working with large-scale ML pipelines
- ML Engineers deploying models into production environments
- Solutions Architects focusing on AI/ML workloads
- Developers integrating ML-powered applications into cloud systems
If you have at least a couple of years of experience with ML or deep learning projects on AWS, this credential validates your ability to build, optimize, and operationalize ML systems following best practices.
What career opportunities can I pursue with the AWS Machine Learning Specialty certification?
This is considered a highly regarded credential for advanced cloud and ML roles, opening the door to exciting opportunities such as:
- Machine Learning Engineer
- Data Scientist
- Applied Scientist
- Cloud AI Engineer
- Solutions Architect specializing in ML workloads
In addition, earning this certification highlights your ability to bring innovation to businesses by deploying intelligent models at scale on AWS, significantly boosting your career credibility and marketability. Given the rise in demand for AI and ML talent globally, this certification is evidence of your readiness to meet that demand.
What is the exam code for the AWS Certified Machine Learning Specialty?
The current exam version is MLS-C01. This is the official AWS exam code, and it’s the version you’ll register for when scheduling your test. The MLS-C01 exam blueprint ensures your skills align with the latest AWS ML services and real-world practices for model development, deployment, tuning, and ongoing operations.
How many questions are on the AWS Certified Machine Learning Specialty exam?
The exam consists of 65 questions in total. These include both multiple-choice questions (one correct answer) and multiple-response questions (two or more correct answers). Importantly, only 50 questions are scored; the other 15 are unscored experimental questions used for future test development. This means you don’t know which ones are unscored, so it’s important to answer every question carefully.
How much time will I have to complete the MLS-C01 exam?
You will have 180 minutes (3 hours) to complete the exam. This generous timeframe is designed to allow you to think critically about real-world style questions and analyze different scenario-based answers. Effective time management is key, so be sure to pace yourself and avoid getting stuck on any single question.
What is the cost of the AWS Certified Machine Learning Specialty exam?
The exam fee is $300 USD. Depending on your country, local taxes or exchange rates may apply. If you already hold an active AWS Certification, you’re also eligible for an exclusive 50% discount on your next AWS Certification exam, which you can claim directly through your AWS Certification account.
In which languages can I take the AWS MLS-C01 exam?
You can take the exam in English, Japanese, Korean, and Simplified Chinese. AWS has designed the certification to be globally accessible, empowering professionals in multiple regions to validate their ML expertise in the language they’re most comfortable with.
What’s the required passing score for the AWS Machine Learning Specialty?
To pass the MLS-C01 exam, you’ll need a minimum scaled score of 750 out of 1000. AWS uses a compensation-based scoring model, meaning you do not have to pass each domain individually; your overall score is what determines success. This allows you to leverage your strengths across certain domains to balance areas where you may be less familiar.
What key exam domains are covered in the AWS MLS-C01 exam?
The MLS-C01 exam blueprint is divided into four weighted domains, each representing critical skills in machine learning:
- Data Engineering (20%)
Focus on creating data repositories, ingestion pipelines, and transformations for ML workloads. - Exploratory Data Analysis (24%)
Covers data cleaning, feature engineering, exploratory analysis, and data visualization. - Modeling (36%)
The largest content area, requiring you to frame business problems, select algorithms, train models, tune hyperparameters, and evaluate performance. - Machine Learning Implementation and Operations (20%)
Deploying, monitoring, and securing ML models at scale in the AWS environment.
Understanding the relative weightings helps you prioritize study areas effectively.
How long is the AWS Machine Learning Specialty certification valid?
Once earned, your certification is valid for 3 years. To maintain it, you’ll need to recertify by passing the latest MLS-C01 (or current version at the time) before it expires. Alternatively, you may choose to pursue another higher-level AWS certification that satisfies recertification requirements.
What level of experience is recommended before taking this exam?
AWS recommends at least 2 or more years of experience developing, architecting, or running ML or deep learning workloads in the AWS Cloud. While there are no required prerequisites, candidates often benefit from having earned other certifications such as:
- AWS Certified Solutions Architect – Associate
- AWS Certified Data Engineer – Associate
- AWS Certified Machine Learning Engineer – Associate
This background ensures familiarity with the AWS ecosystem before diving into ML-specific domains.
Do I need to be a math or deep learning expert to pass?
Not at all. The MLS-C01 exam does not focus on complex math proofs or designing deep learning algorithms from scratch. Instead, it tests applied knowledge, such as selecting the right AWS services, deploying models in production, performing feature engineering, tuning hyperparameters, and monitoring model performance. Having a grasp of ML fundamentals and cloud implementation strategies is far more important than advanced mathematical expertise.
Can I take the MLS-C01 exam online?
Yes! You have two testing options:
- Online proctoring from the comfort of your home or office (requires a webcam, stable internet connection, and private space).
- In-person testing at any Pearson VUE authorized testing center.
This flexibility allows you to choose the method that best fits your schedule and environment.
What kinds of machine learning algorithms should I know for the exam?
You should be familiar with the intuition and use cases of commonly used algorithms such as:
- Logistic Regression, Linear Regression
- Decision Trees and Random Forests
- K-Means clustering
- XGBoost and Ensemble methods
- Neural Networks (RNN, CNN, LLMs)
- Transfer Learning and Foundation Models
While you don’t need to go into heavy mathematical detail, understanding when to use each model and their trade-offs is essential.
What AWS services are in scope for the Machine Learning Specialty exam?
The MLS-C01 test covers a wide range of in-scope AWS services, particularly those used in data workflows and ML. Some of these include:
- Amazon SageMaker (end-to-end ML service)
- Amazon Rekognition, Transcribe, Polly, Translate, Comprehend (AI services)
- AWS Glue, Amazon EMR, Amazon Kinesis, and Amazon Athena (Data engineering)
- Amazon QuickSight (Visualization)
- Amazon Bedrock (foundation models and generative AI)
Learning these services in context of ML workloads will help you effectively tackle scenario-based questions.
What preparation strategies are most effective for the AWS Machine Learning Specialty exam?
The most successful candidates combine a mix of theoretical knowledge, hands-on experience, and practice testing. Recommended strategies include:
- Explore AWS documentation and whitepapers on ML services and best practices.
- Get hands-on with AWS Free Tier experiments in SageMaker, Glue, and related services.
- Reinforce concepts through top-rated AWS Certified Machine Learning Specialty practice exams, which simulate real exam scenarios and help you identify knowledge gaps.
- Use AWS Cloud Quest and AWS Skill Builder for self-paced learning paths designed for this specialty.
What job skills does this certification validate?
This credential validates your ability to:
- Architect and optimize ML pipelines on AWS
- Frame business challenges as ML problems with appropriate models
- Implement feature engineering, model training, and tuning strategies
- Deploy production-ready ML systems following AWS security and scalability best practices
- Troubleshoot, monitor, and retrain models effectively
Employers value certified professionals who can take an ML project from concept to successful deployment.
What makes the AWS Certified Machine Learning Specialty exam valuable in the job market?
Industry reports predict demand for AI and ML professionals to grow significantly in the coming years, making this certification a powerful differentiator. It proves to employers that you can deliver ML solutions in one of the most widely adopted cloud platforms. Having this certification not only boosts your technical credibility but also shows that you can effectively innovate in one of the most high-impact areas of cloud computing.
How often are unscored questions included, and should I worry about them?
Yes, the exam always includes 15 unscored questions. These questions help AWS evaluate potential new items for future exams. Although they do not count toward your score, you won’t know which ones they are. That’s why it is important to treat every question as scored and give each your full attention.
What are AWS best practices I should focus on for this exam?
Many exam questions are built around AWS Well-Architected Framework best practices, especially for ML workloads. You should understand topics like:
- Deploying across multiple Availability Zones (high availability)
- Using Auto Scaling, load balancing, and resource right-sizing
- IAM policies and encryption for security and compliance
- Monitoring with CloudWatch and CloudTrail
These operational and security best practices are essential to scoring well on the exam and succeeding in real-world projects.
Where can I register for the AWS Certified Machine Learning Specialty exam?
You can schedule the exam directly through the official AWS Certified Machine Learning Specialty page. From there, you’ll be guided through choosing your delivery method, selecting a date and time, and completing your exam payment.
Preparing thoroughly and scheduling in advance ensures you approach exam day with confidence and focus.
The AWS Certified Machine Learning Specialty certification is a remarkable opportunity to showcase your expertise in one of the fastest-growing fields in cloud technology. By mastering both the practical and theoretical ML concepts on AWS, you can unlock high-impact careers, contribute to innovative projects, and stay ahead in the evolving future of AI.
