Machine learning is transforming how organizations analyze data, automate processes, personalize customer experiences, and build intelligent applications. As businesses adopt AI at scale, they need professionals who can develop, deploy, monitor, and maintain reliable machine learning solutions.
The AWS Certified Machine Learning Engineer – Associate (MLA-C02) certification validates the skills required to implement and operationalize machine learning and AI workloads on AWS. The updated exam expands its focus beyond traditional machine learning to include generative AI, foundation models, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows.
This certification is relevant to machine learning engineers, MLOps engineers, data engineers, software developers, data scientists, and cloud professionals working with AI solutions.
In this guide, you will learn about the MLA-C02 exam structure, official domains and weightings, key AWS services, preparation strategies, a four-week study plan, and five practice questions.
What Is the AWS MLA-C02 Certification?
The AWS Certified Machine Learning Engineer – Associate certification is designed for professionals who build and operationalize machine learning and AI solutions in production environments.
The updated MLA-C02 exam validates the ability to:
- Prepare and transform data for machine learning and AI workloads
- Select, train, evaluate, and tune ML models
- Work with foundation models and generative AI solutions
- Deploy and orchestrate ML and AI workflows
- Implement MLOps and CI/CD pipelines
- Monitor model performance and inference workloads
- Optimize infrastructure costs and performance
- Apply security and responsible AI practices
The certification covers services such as Amazon SageMaker AI, Amazon Bedrock, Amazon S3, AWS Glue, AWS Lambda, Amazon CloudWatch, and other AWS services used in machine learning workflows.
Who Should Take the MLA-C02 Exam?
The MLA-C02 certification is suitable for professionals who work with machine learning systems and cloud-based AI workloads.
It may be particularly useful for:
- Machine Learning Engineers
- MLOps Engineers
- LLMOps Engineers
- Data Engineers
- Software Developers integrating AI into applications
- Data Scientists moving into production ML engineering
- Machine Learning Architects
- AWS Solutions Architects working with AI workloads
AWS recommends approximately one year of experience using Amazon SageMaker AI, Amazon Bedrock, and related AWS services for machine learning engineering, along with experience in a related technical role.
Candidates should ideally have experience with both traditional machine learning and generative AI concepts.
AWS MLA-C02 Exam Overview
Before beginning your preparation, understand the exam format and requirements.
| Exam Information | Details |
|---|
| Certification | AWS Certified Machine Learning Engineer – Associate |
| Exam Code | MLA-C02 |
| Exam Identifier on AWS Overview | ME1-C02 |
| Certification Provider | Amazon Web Services |
| Certification Level | Associate |
| Exam Duration | 170 minutes |
| Number of Questions | 85 |
| Question Types | Multiple choice and multiple response |
| Passing Score | 720 out of 1,000 |
| Beta Price | $75 USD |
| Delivery | Pearson VUE test center or online proctoring |
| Beta Language | English |
| Certification Validity | 3 years |
Important: MLA-C02 began beta delivery on September 29, 2026. The standard general-availability release is scheduled for January 14, 2027. Beta pricing and delivery details may differ from the final standard exam. Confirm current information through AWS Certification before registering.
AWS MLA-C02 Exam Domains and Weightings
The MLA-C02 exam contains four content domains. The domain structure remains consistent with the previous version, but the task statements have been updated to include AI and generative AI workloads.
| Domain | Exam Weight |
|---|
| Domain 1: Data Preparation for ML and AI | 28% |
| Domain 2: ML Model and Foundation Model Development | 24% |
| Domain 3: Deployment and Orchestration of ML and AI Workflows | 24% |
| Domain 4: Operating, Monitoring, and Securing ML and AI Solutions | 24% |
These percentages represent the scored exam content. Use them to prioritize preparation while ensuring that you study all four domains.
MLA-C02 Exam Syllabus and Important Topics
Domain 1: Data Preparation for ML and AI – 28%
Data preparation is the highest-weighted domain in the MLA-C02 exam. It focuses on collecting, transforming, validating, and preparing data for machine learning and AI workloads.
Important topics include:
- Data ingestion and collection
- Data storage and data repositories
- Data transformation and preprocessing
- Data quality and validation
- Data labeling
- Feature engineering
- Feature selection
- Data leakage
- Data versioning
- Data preparation for traditional ML
- Data preparation for generative AI and foundation models
- Data privacy and responsible data handling
Relevant AWS services and tools may include:
- Amazon S3
- AWS Glue
- Amazon SageMaker Data Wrangler
- Amazon SageMaker Feature Store
- AWS Lambda
- Amazon Kinesis
- Amazon Redshift
Preparation tip: Understand how to select suitable data ingestion, storage, and transformation services. Practice identifying data quality problems, data leakage, and preprocessing requirements for different ML use cases.
Domain 2: ML Model and Foundation Model Development – 24%
This domain evaluates the ability to develop, train, evaluate, and improve traditional ML models and foundation model-based solutions.
Important topics include:
- Selecting suitable ML algorithms
- Model training and experimentation
- Hyperparameter tuning
- Model evaluation metrics
- Bias and variance
- Overfitting and underfitting
- Model versioning
- Model selection
- Foundation model selection
- Prompt engineering
- Foundation model customization and fine-tuning
- Retrieval-augmented generation (RAG)
- Generative AI evaluation
- Responsible AI practices
Relevant AWS services and tools may include:
- Amazon SageMaker AI
- Amazon SageMaker Training
- Amazon SageMaker Automatic Model Tuning
- Amazon SageMaker Model Registry
- Amazon Bedrock
- Amazon Bedrock Knowledge Bases
- Amazon Bedrock evaluation capabilities
Preparation tip: Understand when to use traditional ML models versus foundation models. Learn how to select evaluation metrics based on the business problem and how to identify overfitting, underfitting, and model performance issues.
Domain 3: Deployment and Orchestration of ML and AI Workflows – 24%
This domain focuses on deploying ML and AI models and automating the workflows required to move solutions into production.
Important topics include:
- Model deployment strategies
- Real-time inference
- Batch inference
- Asynchronous inference
- Serverless inference
- Deployment infrastructure
- Model endpoints
- Containerized ML workloads
- Workflow orchestration
- CI/CD pipelines
- MLOps automation
- Model versioning and release management
- GenAI application deployment
- Agentic AI workflow orchestration
Relevant AWS services and tools may include:
- Amazon SageMaker AI endpoints
- Amazon SageMaker Pipelines
- AWS Step Functions
- AWS CodePipeline
- AWS CodeBuild
- AWS Lambda
- Amazon Bedrock
- Amazon Elastic Container Service (Amazon ECS)
- Amazon Elastic Kubernetes Service (Amazon EKS)
Preparation tip: Learn the differences between real-time, batch, asynchronous, and serverless inference. Practice choosing the most appropriate deployment option based on latency, throughput, cost, and workload requirements.
Domain 4: Operating, Monitoring, and Securing ML and AI Solutions – 24%
This domain evaluates the ability to operate ML and AI workloads reliably, monitor performance, optimize costs, and protect systems and data.
Important topics include:
- Model inference monitoring
- Model performance monitoring
- Data drift and model drift
- Model quality evaluation
- Logging and observability
- Infrastructure performance
- Cost optimization
- Security and access management
- Encryption and data protection
- Responsible AI
- Model governance
- Monitoring generative AI applications
- Monitoring agentic AI workflows
- Operational troubleshooting
Relevant AWS services and tools may include:
- Amazon CloudWatch
- AWS CloudTrail
- AWS IAM
- AWS KMS
- Amazon SageMaker Model Monitor
- Amazon SageMaker Clarify
- Amazon Bedrock Guardrails
- AWS Cost Explorer
Preparation tip: Understand how to monitor production ML systems and identify performance degradation. Learn how IAM, encryption, logging, and responsible AI controls help secure ML and generative AI workloads.
What Is New in MLA-C02?
The updated MLA-C02 exam reflects how machine learning engineering roles have expanded to include production generative AI and foundation model workloads.
1. Generative AI and Foundation Models
Candidates should understand how to select, customize, deploy, and operationalize foundation models.
Topics include:
- Foundation model selection
- Prompt engineering
- Model customization
- Fine-tuning
- Model evaluation
- Generative AI application architecture
2. Retrieval-Augmented Generation (RAG)
RAG combines a foundation model with external knowledge sources to provide more relevant, context-aware responses.
Candidates should understand:
- Document ingestion
- Text chunking
- Embeddings
- Vector search
- Retrieval
- Context augmentation
- RAG evaluation
3. Agentic AI Workflows
The updated exam includes concepts related to AI agents and complex workflows.
Important areas include:
- Agent orchestration
- Tool use
- Workflow coordination
- Multi-step task execution
- Monitoring agent behavior
- Security and governance of agentic systems
4. Expanded Amazon Bedrock Coverage
Amazon Bedrock is an important service for building generative AI applications on AWS.
Candidates should understand how it supports foundation model access, application development, knowledge bases, guardrails, and related generative AI workflows.
5. Responsible AI
Responsible AI concepts apply to both traditional ML and generative AI.
Study topics such as:
- Bias and fairness
- Explainability
- Privacy
- Safety
- Human oversight
- Model limitations
- Appropriate evaluation
- Governance and monitoring
Effective Preparation Strategies for the AWS MLA-C02 Exam
1. Start with the Official Exam Guide
Download the current AWS MLA-C02 exam guide and review all four domains.
Use the official task statements as a checklist to track your progress. Avoid relying entirely on older MLA-C01 materials because MLA-C02 includes expanded generative AI and foundation model content.
2. Build Strong Machine Learning Fundamentals
Review essential ML concepts before moving into advanced AWS services.
Focus on:
- Supervised and unsupervised learning
- Classification and regression
- Clustering
- Training and validation datasets
- Model evaluation metrics
- Overfitting and underfitting
- Hyperparameter tuning
- Feature engineering
3. Practice AWS Data Preparation Services
Understand how AWS services support data ingestion, transformation, storage, and feature engineering.
Practice selecting suitable services for different data types, data volumes, and ML requirements.
4. Learn Amazon SageMaker AI
Amazon SageMaker AI is a central service for machine learning engineering on AWS.
Understand its capabilities for:
- Data preparation
- Model training
- Hyperparameter tuning
- Model registry
- Model deployment
- Inference
- Model monitoring
- ML workflow automation
5. Study Amazon Bedrock and Generative AI
Because MLA-C02 expands coverage of generative AI, candidates should understand how to build and operationalize foundation model-based applications.
Review:
- Foundation model selection
- Prompt engineering
- RAG architectures
- Knowledge bases
- Model evaluation
- Guardrails
- Agentic AI workflows
- Responsible AI practices
6. Practice MLOps and Deployment Scenarios
Learn how to automate model training, testing, deployment, and monitoring.
Understand the role of CI/CD pipelines, workflow orchestration, model registries, and deployment strategies.
7. Take Practice Exams
Practice questions help you identify knowledge gaps and improve time management.
After each practice test:
- Review incorrect answers.
- Identify the relevant exam domain.
- Revisit the underlying AWS service or ML concept.
- Practice similar scenarios.
- Track progress over time.
Four-Week AWS MLA-C02 Study Plan
This four-week plan provides a structured approach to preparing for the exam.
| Week | Topics | Study Activities |
|---|
| Week 1 | Data Preparation for ML and AI | Study data ingestion, storage, transformation, validation, feature engineering, data quality, and data leakage. |
| Week 2 | ML Model and Foundation Model Development | Review ML algorithms, evaluation metrics, hyperparameter tuning, model selection, foundation models, prompt engineering, and RAG. |
| Week 3 | Deployment and Orchestration | Practice model deployment, inference options, SageMaker Pipelines, CI/CD, workflow orchestration, and generative AI application deployment. |
| Week 4 | Operations, Monitoring, Security, and Revision | Study model monitoring, drift, observability, security, cost optimization, responsible AI, and complete timed practice tests. |
Recommended Daily Study Routine
A daily schedule of approximately two hours can help you maintain steady progress.
- 30 minutes: Review ML and AI concepts.
- 30 minutes: Study AWS services and architecture.
- 40 minutes: Complete hands-on labs or scenario questions.
- 20 minutes: Revise weak areas and update notes.
Candidates with limited AWS experience may need additional time for hands-on practice.
AWS MLA-C02 Practice Questions and Answers
The following five original practice questions cover important concepts from the MLA-C02 exam domains. These are learning examples, not actual AWS examination questions.
Question 1: Data Preparation
A machine learning engineer is preparing a dataset for model training. The dataset contains missing numerical values, and the team wants to replace them with the median value of each feature. Which data preparation approach is most appropriate?
A. Remove every record containing a missing value
B. Apply median imputation to the numerical features
C. Use the target variable to fill every missing feature value
D. Convert all numerical features into text
Correct Answer: B. Apply median imputation to the numerical features
Explanation: Median imputation replaces missing numerical values with the median of the corresponding feature. It can be useful when the data contains outliers because the median is less sensitive to extreme values than the mean.
Question 2: Model Evaluation
A machine learning engineer is developing a binary classification model for detecting a rare but important security event. Missing a positive event is significantly more costly than investigating a false alarm. Which metric should receive particular attention?
A. Accuracy
B. Recall
C. Mean squared error
D. Training duration
Correct Answer: B. Recall
Explanation: Recall measures the proportion of actual positive cases correctly identified by the model. When missing a positive event is costly, recall is especially important. The engineer should still evaluate precision and other relevant metrics to understand the trade-offs.
Question 3: Generative AI and RAG
A company wants a foundation model to answer questions using internal documents that are updated frequently. The company wants to avoid retraining the model whenever documents change. Which approach is most suitable?
A. Train a new foundation model after every document update
B. Use a retrieval-augmented generation architecture
C. Remove the internal documents from the application
D. Increase the model's training duration
Correct Answer: B. Use a retrieval-augmented generation architecture
Explanation: RAG retrieves relevant information from an external knowledge source and provides it as context to the foundation model. This allows the knowledge base to be updated without retraining the foundation model for every document change.
Question 4: ML Deployment
A machine learning application requires low-latency predictions for individual user requests. The model must remain available to respond to incoming requests. Which deployment option is most appropriate?
A. Batch inference
B. Real-time inference endpoint
C. Offline model evaluation
D. Manual model export
Correct Answer: B. Real-time inference endpoint
Explanation: A real-time inference endpoint is designed to provide low-latency predictions for incoming requests. Batch inference is more suitable for processing large volumes of data without requiring immediate responses.
Question 5: Model Monitoring
A deployed ML model begins producing lower-quality predictions because the characteristics of incoming data have changed over time. Which monitoring concept is most relevant?
A. Data drift monitoring
B. Increasing the number of database tables
C. Disabling model logging
D. Reducing all model features to one column
Correct Answer: A. Data drift monitoring
Explanation: Data drift occurs when the distribution or characteristics of incoming data change compared with the data used during model training. Monitoring data drift can help identify when a model may require investigation, retraining, or other corrective action.
Frequently Asked Questions (FAQs)
1. What is the AWS MLA-C02 certification?
The AWS Certified Machine Learning Engineer – Associate certification validates the ability to build, deploy, operationalize, monitor, and maintain machine learning and AI solutions on AWS.
2. How many questions are included in the MLA-C02 exam?
The MLA-C02 beta exam contains 85 questions and has a duration of 170 minutes.
3. What is the passing score for MLA-C02?
The published passing score is 720 out of 1,000.
4. What are the four domains of the MLA-C02 exam?
The four domains are:
- Data Preparation for ML and AI
- ML Model and Foundation Model Development
- Deployment and Orchestration of ML and AI Workflows
- Operating, Monitoring, and Securing ML and AI Solutions
5. What is the difference between MLA-C01 and MLA-C02?
MLA-C02 retains the four-domain structure but updates the exam content to include generative AI, foundation models, LLMs, RAG architectures, agentic AI workflows, expanded Amazon Bedrock coverage, and responsible AI practices.
6. Is Amazon Bedrock included in MLA-C02?
Yes. Amazon Bedrock is an important service in the updated exam, particularly for foundation model-based applications, generative AI, knowledge bases, and related workflows.
7. Is prior AWS experience recommended for MLA-C02?
Yes. AWS recommends approximately one year of experience using Amazon SageMaker AI, Amazon Bedrock, and other AWS services for ML engineering, along with experience in a related technical role.
8. How should I prepare for the MLA-C02 exam?
Review the official exam guide, strengthen ML fundamentals, practice AWS data preparation and model development services, study SageMaker AI and Amazon Bedrock, complete hands-on labs, and take timed practice tests.
Conclusion
The AWS Certified Machine Learning Engineer – Associate MLA-C02 certification is a valuable credential for professionals who want to demonstrate their ability to build and operate production-grade machine learning and AI solutions on AWS.
The updated exam reflects the evolving role of machine learning engineers by combining traditional ML engineering with generative AI, foundation models, RAG, agentic AI, and responsible AI practices.
To prepare effectively, focus on all four exam domains, gain hands-on experience with AWS services, practice realistic ML engineering scenarios, and revise your weaker areas consistently.
Start your AWS MLA-C02 exam preparation with Clearcatnet.com and take the next step toward advancing your machine learning and AI career.
Written By:Tannu Singh
Published on: 31/10/2026
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