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AWS Certified Machine Learning Engineer Associate Quick Facts
The AWS Certified Machine Learning Engineer Associate certification empowers you to master practical ML skills with confidence by guiding you through the most essential concepts and real-world applications. This exam overview highlights everything you need to know to prepare effectively and succeed with clarity and purpose.
What does the AWS Certified Machine Learning Engineer Associate certification validate?
This certification demonstrates your ability to build, train, deploy, and monitor machine learning models on AWS. It validates your expertise in applying data preparation techniques, selecting and training appropriate models, streamlining ML workflows with orchestration tools, and ensuring solutions remain secure, scalable, and cost-effective. It is ideal for professionals who want to reliably transform raw data into impactful ML solutions using the rich ecosystem of AWS services like SageMaker, Glue, Bedrock, and more.
Who should pursue the AWS Certified Machine Learning Engineer - Associate certification?
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification is an excellent fit for professionals who want to validate their real-world skills in building, deploying, and maintaining machine learning solutions on AWS. This certification is ideal if you already have about one year of hands-on experience in ML engineering roles or if you work in related technical positions like backend development, DevOps, data engineering, or data science.
If you’re passionate about operationalizing ML workflows and leveraging Amazon SageMaker and other AWS ML services to deliver business value, this certification will strongly position you for success. It’s also particularly valuable for those aiming to bridge the gap between software engineering, machine learning, and cloud deployment.
What types of roles can benefit from AWS Certified Machine Learning Engineer - Associate?
Earning this certification can help you qualify for a broad spectrum of technical roles where machine learning meets cloud infrastructure. These include:
- Machine Learning Engineer
- MLOps Engineer
- Data Engineer
- Backend Software Engineer (with a focus on ML workloads)
- Data Scientist expanding into production-level ML
- DevOps Engineer with an interest in ML automation and pipelines
By showcasing verified expertise, you can confidently pursue opportunities in AI-driven organizations where ML skills are in high demand.
How many questions are on the AWS MLA-C01 exam?
The exam consists of 65 questions in total. Out of these, 50 questions are scored and contribute to your final exam result, while 15 unscored questions are included to collect data for future test versions. These unscored questions don’t count against you, but you won’t know which ones are unscored during the test.
The exam includes multiple-choice, multiple-response, ordering, matching, and scenario-based case study questions. This variety ensures that AWS is assessing deep understanding of ML engineering practices rather than simple memorization.
How much time is available for the AWS Certified Machine Learning Engineer Associate exam?
You will have 130 minutes to complete the AWS Certified Machine Learning Engineer Associate exam. This time limit provides plenty of opportunity to thoroughly analyze the scenario-driven questions, which often involve practical implementation details.
Time management is important, especially when questions include case studies or ordering tasks. A good strategy is to pace yourself by aiming for roughly 2 minutes per question and flagging any that require deeper thought to revisit later.
What is the passing score for the AWS MLA-C01 certification?
The exam is scored on a scale of 100–1000, and a minimum passing score of 720 is required. This scaled scoring system ensures fairness across different sets of exam questions that may vary slightly in difficulty.
Importantly, the exam uses a compensatory scoring model. This means you only need to achieve a passing score overall—you don’t need to pass each domain individually. Strong performance in one domain can balance out weaker performance in another.
How much does it cost to take the AWS Certified Machine Learning Engineer - Associate exam?
The exam fee is $150 USD. Depending on your region, additional taxes or local currency exchange rates may apply.
This cost represents an investment in your professional career. Considering the high demand for ML job roles globally, earning this certification can quickly pay off by boosting your competitiveness in the job market.
What languages is the AWS MLA-C01 exam available in?
The exam can be taken in multiple languages to support a global audience. The currently available languages are:
- English
- Japanese
- Korean
- Simplified Chinese
This makes the certification widely accessible to professionals worldwide who are looking to leverage AWS ML services.
Which version of the AWS Machine Learning Engineer - Associate exam should I take?
The current and active version of the exam is MLA-C01. When preparing, always make sure your study materials specifically mention MLA-C01, since earlier beta versions may not reflect the same structure or updated content.
Because AWS continuously innovates its services, certification exams are periodically updated. Staying aligned with the most recent version ensures your knowledge matches the latest AWS service capabilities and best practices.
What topics and domains does the MLA-C01 exam cover?
The exam content spans four main domains that reflect how ML practitioners use AWS services in production-ready settings:
-
Data Preparation for Machine Learning – 28%
Covers data ingestion, storage, transformation, feature engineering, and integrity checks for ML readiness. -
ML Model Development – 26%
Focuses on selecting model approaches, training and tuning, and analyzing model performance. -
Deployment and Orchestration of ML Workflows – 22%
Involves deploying models, provisioning infrastructure, and setting up CI/CD pipelines for ML. -
Monitoring, Maintenance, and Security – 24%
Includes monitoring inference performance, controlling infrastructure costs, and applying AWS security best practices.
These weightings mean that data preparation and ML lifecycle management are equally important as training and deployment skills.
How difficult is the AWS Certified Machine Learning Engineer Associate exam?
Many candidates find this exam to be highly practical and rewarding. It emphasizes real-world ML engineering tasks such as feature engineering, scaling workloads, monitoring models, and implementing CI/CD practices with SageMaker.
To prepare effectively, hands-on practice in AWS is invaluable. While theoretical knowledge is important, using services like SageMaker, CloudWatch, and CodePipeline in a real AWS environment will give you confidence going into test day.
What knowledge of AWS services is needed?
To succeed, you should be comfortable with key ML-focused AWS services and supporting tools, including:
- Amazon SageMaker for model development, training, and deployment.
- AWS Glue, Amazon S3, and Amazon Redshift for data preparation and storage.
- CodePipeline / CodeBuild / CodeDeploy for ML CI/CD automation.
- Amazon CloudWatch and SageMaker Model Monitor for monitoring ML solutions.
- IAM, KMS, and VPC networking for securing ML environments.
Familiarity with MLOps practices such as automated retraining, integration testing, and cost optimization with tools like AWS Budgets is also expected.
Are there any prerequisites for taking the AWS MLA-C01 exam?
There are no mandatory prerequisites. However, AWS recommends:
- At least 1 year of experience using Amazon SageMaker and related ML-focused AWS services.
- Hands-on experience in roles like ML engineering, DevOps, data engineering, or backend development.
- Understanding of ML algorithms, fundamentals of data pipelines, and cloud deployment concepts.
If you are relatively new to machine learning, AWS offers training and exam prep plans that can help you gain the core skills before attempting the exam.
What kind of ML algorithms and techniques does this exam focus on?
Although you don’t need to be a deep ML researcher, you should have a fundamental understanding of commonly used ML algorithms and use cases. This includes supervised and unsupervised techniques, neural networks, tree-based models, and basic evaluation metrics like precision, recall, and AUC.
More importantly, the exam focuses on applying these algorithms in practical AWS environments: preparing data, deploying models at scale, performing hyperparameter tuning, and monitoring results in production.
Where can I take the AWS Machine Learning Engineer Associate exam?
There are two options for taking the exam:
- Online with remote proctoring through Pearson VUE. This requires a working webcam, stable internet, and a quiet environment.
- In-person at a Pearson VUE Test Center, where you complete your exam under proctor supervision.
Both options provide the same certification results, so you can choose whichever approach works best for your schedule and environment.
How long is the AWS MLA-C01 certification valid?
The AWS Certified Machine Learning Engineer Associate certification is valid for 3 years.
To maintain active certification status, you can either retake the updated version of the exam or earn a more advanced AWS certification. Keeping your certification current also demonstrates to employers that your ML and AWS expertise is up to date with the latest tools and industry standards.
How should I study for the AWS MLA-C01 exam?
The best preparation strategy includes both study and hands-on practice. Key approaches are:
- Completing AWS digital training and guided Exam Prep Plans.
- Reviewing AWS machine learning whitepapers and architectural documentation.
- Practicing with SageMaker, Glue, CodePipeline, and CloudWatch directly in AWS to gain real-world familiarity.
- Reinforcing your knowledge with high-quality AWS Certified Machine Learning Engineer Associate practice exams that fully simulate the actual exam environment and provide detailed explanations.
By combining structured study with hands-on practice, you’ll build confidence and maximize your readiness for test day.
What kind of question formats appear on the exam?
Unlike other associate exams that may only use multiple-choice, the AWS MLA-C01 includes a variety of question types, such as:
- Multiple Choice: Choose a single correct answer.
- Multiple Response: Select two or more correct answers.
- Ordering: Arrange steps to match an ML workflow or process.
- Matching: Pair concepts, tools, or approaches correctly.
- Case Studies: Read a scenario and answer multiple related questions.
This mix of formats reflects real-world problem solving rather than rote memorization.
How does the MLA-C01 differ from the AWS Machine Learning - Specialty certification?
The AWS Certified Machine Learning Engineer - Associate exam is role-based, focusing narrowly on the responsibilities of ML engineers and MLOps practitioners. It is designed for individuals with at least one year of applied ML experience.
The AWS Machine Learning - Specialty certification is broader in scope. It covers data engineering, analytics, and advanced ML research topics. It’s often better suited for data scientists and highly experienced professionals with 2 or more years of ML expertise.
What comes after the AWS Certified Machine Learning Engineer - Associate certification?
After achieving MLA-C01, many learners choose to advance to the AWS Certified Machine Learning - Specialty for deeper expertise. Others pursue professional-level certifications such as AWS Solutions Architect Professional or AWS DevOps Engineer Professional, depending on career goals.
This certification also serves as a strong base for hybrid career growth. For example, you could transition toward solutions architecture with an ML focus or become a thought leader in MLOps practices across organizations.
Can the AWS MLA-C01 really boost my career?
Absolutely! According to the World Economic Forum, demand for machine learning and AI specialists is expected to grow significantly. At the same time, many companies struggle to find professionals who can both develop ML models and operationalize them in production.
This certification validates that you are job-ready. It proves to employers that you can design, deploy, and maintain ML workflows on AWS, giving you an edge in competitive job markets worldwide.
Where can I register for the AWS Certified Machine Learning Engineer Associate exam?
You can register directly through the official AWS Certified Machine Learning Engineer - Associate page. The registration process is simple: sign in to your AWS Certification account, select the exam, choose your testing option (online or in-person), pick the date and time, and pay the exam fee.
Preparing well and securing your exam slot puts you on the fast track to earning one of the most exciting certifications in cloud-driven machine learning.
The AWS Certified Machine Learning Engineer - Associate credential is a smart choice for professionals eager to stand at the intersection of machine learning and cloud innovation. With the right preparation, study resources, and hands-on practice, you can confidently achieve this certification and elevate your career in AI and ML.
