| < | All IT Certification |
| Exam Code: | MLA-C02 |
| Exam Name: | AWS Certified Machine Learning Engineer - Associate |
| Updated Time: | 2026-10-23 |
| Q&As: | 357 Q&As |
| Free MLA-C02 Demo DownLoad | |
The AWS Certified Machine Learning Engineer - Associate (MLA-C02) exam validates the ability to build, operationalize, deploy, and maintain AI and machine learning solutions and pipelines on AWS. The updated exam covers both traditional machine learning models and foundation models, reflecting the expanding role of machine learning engineers in generative AI and production AI systems.
Candidates should be prepared to work with data preparation, machine learning and foundation model development, deployment and orchestration, monitoring, security, and operational optimization. MLA-C02 also expands coverage of technologies and practices such as Amazon Bedrock, generative AI, retrieval-augmented generation (RAG), foundation models, LLMs, agentic AI workflows, and responsible AI.
Passcert provides updated MLA-C02 Practice Tests and exam preparation materials to help candidates review these knowledge areas, identify weak topics, and supplement the official AWS exam guide, training resources, and hands-on experience.
Detail
Current MLA-C02 Beta Information
Certification
AWS Certified Machine Learning Engineer - Associate
Exam Code
MLA-C02
Status
Beta registration open
Beta Delivery Begins
September 29, 2026
Questions
85
Duration
170 minutes
Beta Price
$75 USD
Language
English
Delivery
Pearson VUE test center or online proctoring
General Availability
January 14, 2027
Focus on collecting and storing data, transforming and preprocessing datasets, performing feature engineering, validating data quality, and managing bias. Candidates should understand how data preparation requirements differ across traditional ML and modern AI workloads.
Review traditional ML model development together with foundation model selection, customization, evaluation, and generative AI development. This domain reflects one of the most important expansions from MLA-C01 to MLA-C02.
Focus on deploying ML and AI solutions, automating workflows, orchestrating pipelines, and operating production workloads across traditional machine learning and generative AI environments.
Review monitoring, troubleshooting, performance and cost optimization, security controls, governance, and the operational practices required to maintain reliable ML and AI solutions.
Passcert MLA-C02 Practice Tests provide structured question-based review across the four content domains of the updated AWS Certified Machine Learning Engineer - Associate exam. Candidates can use the materials to reinforce both traditional machine learning engineering skills and newer AI concepts involving foundation models, Amazon Bedrock, RAG, and agentic workflows.
Use MLA-C02 practice questions throughout your preparation rather than only during final review. After studying each domain, complete related questions, analyze incorrect responses, and return to the relevant AWS documentation or hands-on exercises before repeating weak areas.
The preparation package helps you:
A balanced preparation plan should combine the official exam guide, structured learning, practical experience, and regular practice.
Review data collection, storage, transformation, preprocessing, feature engineering, data quality, and bias management. Practise choosing appropriate AWS storage and data services based on workload, performance, cost, and compliance requirements.
Maintain a strong understanding of traditional model training and evaluation while expanding your knowledge of foundation models, LLMs, model selection, customization, fine-tuning, prompt-related workflows, and model evaluation.
Pay particular attention to the newer MLA-C02 areas. Understand Amazon Bedrock, retrieval-augmented generation, embeddings, vector search, knowledge bases, AI agents, and the orchestration of multi-step AI workflows.
Study how ML and AI workloads are deployed, automated, monitored, secured, troubleshot, and optimized in production. Include reliability, scalability, performance, cost management, and responsible AI considerations.
Complete practice questions after reviewing each domain. Analyze incorrect answers, revisit the relevant AWS documentation or hands-on task, and repeat weak areas until you understand the underlying technical concept.
AWS recommends that MLA-C02 candidates have at least one year of experience using Amazon SageMaker AI, Amazon Bedrock, and other AWS services for machine learning engineering, together with at least one year of experience in a related technical role.
Candidates should also have practical exposure to both traditional machine learning and generative AI, because MLA-C02 evaluates the ability to work across both types of production AI workloads.
The preparation materials are organized around the four current MLA-C02 content domains, helping you review both traditional machine learning engineering and newer generative AI skills systematically.
Study with flexible formats designed for different preparation needs. Use the PDF materials for convenient reading and review, while the test engine provides an interactive environment for question practice and progress evaluation.
The MLA-C02 preparation materials are reviewed against relevant AWS exam objective and technology changes. When revisions are required, updated versions are made available according to the applicable product update terms.
After completing your purchase, you can access the available MLA-C02 preparation files through your Passcert Member Area, making it convenient to download and manage your study materials.
Passcert support is available to assist with account access, file downloads, test engine installation, product updates, and other service-related questions during your preparation.
MLA-C02 is the updated AWS Certified Machine Learning Engineer - Associate exam. It validates the ability to build, operationalize, deploy, and maintain traditional ML and AI solutions and pipelines on AWS.
MLA-C02 covers Data Preparation for ML and AI (28%), ML Model and Foundation Model Development (24%), Deployment and Orchestration of ML and AI Workflows (24%), and Operating, Monitoring, and Securing ML and AI Solutions (24%).
Yes. MLA-C02 covers both traditional machine learning and modern AI workloads, including foundation models, generative AI, Amazon Bedrock, RAG, and related AI engineering concepts.
Review the official AWS exam guide, strengthen both traditional ML and generative AI skills, gain hands-on experience with services such as SageMaker AI and Amazon Bedrock, and use MLA-C02 practice questions to identify weak areas.
The preparation materials are reviewed regularly and may be updated when relevant changes are identified in the exam objectives, technologies, certification requirements, or question formats.
Depending on the product, the package may include downloadable PDF study materials and an interactive test engine. The exact contents are displayed on the individual product page.