AWS Certified Machine Learning Engineer Associate Quick Facts (2025)

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.

Exam Domains Covered (Click to expand breakdown)

Exam Domain Breakdown

Domain 1: Data Preparation for Machine Learning (ML) (28% of the exam)

Ingest and store data.

Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
AWS storage options, including use cases and tradeoffs
Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
Making initial storage decisions based on cost, performance, and data structure

Ingest and store data summary:
This section highlights how to bring diverse types of data into AWS and organize it so ML models can consume it smoothly. You will learn how to select the right format like JSON, CSV, or Parquet for performance and cost optimization, and how to leverage services including S3, Kinesis, and EFS to handle both static and streaming workloads. The focus is on practical tradeoffs so you can choose the most efficient approach while ensuring data remains accessible and scalable.

You will also practice combining data from multiple systems and handling issues like capacity or throughput. By deeply understanding ingestion tools such as SageMaker Data Wrangler and AWS Glue, you will be equipped to quickly shape your raw data environment into a reliable foundation for downstream machine learning workflows.

Transform data and perform feature engineering.

Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
Services that transform streaming data (for example, AWS Lambda, Spark)
Data annotation and labeling services that create high-quality labeled datasets
Transforming data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

Transform data and perform feature engineering summary:
This section emphasizes how to shape your dataset into the right form for model training. You will learn effective techniques such as imputing missing values, scaling and normalization, encoding categorical variables, and crafting features that unlock more predictive power. Each technique empowers better model accuracy and reliability while ensuring consistency across varied data types.

AWS services like Glue DataBrew, SageMaker Data Wrangler, and SageMaker Feature Store streamline these processes to save time and enhance productivity. Additionally, annotation tools such as SageMaker Ground Truth help build high-quality labeled datasets. By mastering these methods, you prepare data that is both clean and meaningful, allowing models to learn efficiently and perform optimally.

Ensure data integrity and prepare data for modeling.

Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
Techniques to encrypt data
Data classification, anonymization, and masking
Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)
Validating data quality (for example, by using AWS Glue DataBrew and AWS Glue Data Quality)
Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)

Ensure data integrity and prepare data for modeling summary:
In this section, you refine how to ensure that ML datasets are complete, fair, and trustworthy. You will learn strategies to identify and address issues such as imbalance in target classes, systemic bias in sampling, and variations across data types. With AWS services like SageMaker Clarify, you will detect and reduce bias, creating a stronger foundation for equitable and accurate predictions.

You will also focus on compliance and data security by exploring encryption and anonymization techniques, ensuring protection of sensitive information across ML systems. Combining best practices in quality validation with workflows in Glue Data Quality and DataBrew allows you to uphold dataset integrity throughout the modeling lifecycle. This preparation ensures stronger, more reliable models that can scale with confidence across business environments.

Domain 2: ML Model Development (26% of the exam)

Choose a modeling approach.

Capabilities and appropriate uses of ML algorithms to solve business problems
How to use AWS artificial intelligence (AI) services (for example, Amazon Translate, Amazon Transcribe, Amazon Rekognition, Amazon Bedrock) to solve specific business problems
How to consider interpretability during model selection or algorithm selection
SageMaker built-in algorithms and when to apply them
Assessing available data and problem complexity to determine the feasibility of an ML solution
Comparing and selecting appropriate ML models or algorithms to solve specific problems
Choosing built-in algorithms, foundation models, and solution templates (for example, in SageMaker JumpStart and Amazon Bedrock)
Selecting models or algorithms based on costs
Selecting AI services to solve common business needs

Choose a modeling approach summary:
This section covers how to pick the most suitable algorithms or ML services based on the nature of your problem, interpretability requirements, and cost considerations. You will learn when to apply supervised, unsupervised, or deep learning methods to specific tasks and when built-in SageMaker algorithms or pre-trained foundation models can accelerate progress.

Exploring AWS offerings such as Rekognition for image analysis or Bedrock for foundation models ensures you can align the right service to a business use case effectively. This combination of algorithm knowledge and AWS tooling confidence enables you to craft solutions that are efficient, insightful, and scalable.

Train and refine models.

Elements in the training process (for example, epoch, steps, batch size)
Methods to reduce model training time (for example, early stopping, distributed training)
Factors that influence model size
Methods to improve model performance
Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)
Hyperparameter tuning techniques (for example, random search, Bayesian optimization)
Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network)
Methods to integrate models that were built outside SageMaker into SageMaker
Using SageMaker built-in algorithms and common ML libraries to develop ML models
Using SageMaker script mode with SageMaker supported frameworks to train models (for example, TensorFlow, PyTorch)
Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart)
Performing hyperparameter tuning (for example, by using SageMaker automatic model tuning [AMT])
Integrating automated hyperparameter optimization capabilities
Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection)
Combining multiple training models to improve performance (for example, ensembling, stacking, boosting)
Reducing model size (for example, by altering data types, pruning, updating feature selection, compression)
Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)

Train and refine models summary:
Here you shape raw algorithms into tuned, high-performing models with the aid of AWS ML capabilities. You will explore how hyperparameters affect training, how regularization reduces overfitting, and how training techniques like early stopping save time while boosting accuracy.

The section also emphasizes managing model versions, fine-tuning pre-trained models, and ensembling multiple approaches to achieve stronger overall performance. By integrating automated hyperparameter optimization with tools like SageMaker AMT, you streamline iteration cycles and arrive at models that balance predictive strength, efficiency, and cost-effectiveness.

Analyze model performance.

Model evaluation techniques and metrics (for example, confusion matrix, heat maps, F1 score, accuracy, precision, recall, Root Mean Square Error [RMSE], receiver operating characteristic [ROC], Area Under the ROC Curve [AUC])
Methods to create performance baselines
Methods to identify model overfitting and underfitting
Metrics available in SageMaker Clarify to gain insights into ML training data and models
Convergence issues
Selecting and interpreting evaluation metrics and detecting model bias
Assessing tradeoffs between model performance, training time, and cost
Performing reproducible experiments by using AWS services
Comparing the performance of a shadow variant to the performance of a production variant
Using SageMaker Clarify to interpret model outputs
Using SageMaker Model Debugger to debug model convergence

Analyze model performance summary:
This section ensures you understand how to interpret your trained models with clear metrics and tangible insights. You will work with common evaluation concepts like precision, recall, F1 score, and ROC-AUC to identify tradeoffs and make decisions aligned with your project’s objectives.

The content also covers analyzing convergence, exploring shadow deployments, and using SageMaker Clarify and Debugger to interpret both data integrity and training stability. By pairing statistical evaluation with AWS monitoring tools, you sharpen your ability to confirm model performance before production deployment.

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.

AWS Certified Machine Learning Engineer Associate Quick Facts (2025)

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