AWS Certified Machine Learning Engineer - Associate (MLA-C02) Practice Tests

Use this MLA-C02 practice exam to prepare for AWS Certified Machine Learning Engineer - Associate (MLA-C02) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 321 questions for AWS MLA-C02, so you can review the exam steadily instead of relying on one long cram session.

As you practice, pay extra attention to recurring topics such as Data Preparation for ML and AI, ML Model and Foundation Model (FM) Development, Deployment and Orchestration of ML and AI Workflows, and Operating, Monitoring, and Securing ML and AI Solutions. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.

The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.

The AWS Certified Machine Learning Engineer - Associate (MLA-C02) validates a candidate's ability to build, operationalize, deploy, and maintain AI and ML solutions and pipelines using the AWS Cloud. The exam covers both traditional ML workflows and modern generative AI (GenAI) workloads, including foundation models (FMs), Retrieval Augmented Generation (RAG) architectures, and agentic AI systems. Core AWS services tested include Amazon SageMaker AI, Amazon Bedrock, AWS Glue, Amazon Kinesis, and a range of storage and orchestration services.

MLA-C02 is an updated version of the MLA-C01 exam, expanding significantly into generative AI territory. New content areas include configuring vector databases for AI applications, preparing data for FM fine-tuning and continuous pre-training, deploying and managing Amazon Bedrock agents, implementing RAG pipelines, monitoring agentic workflows, and applying responsible AI safeguards using Amazon Bedrock Guardrails. This makes the certification distinctly relevant for engineers working at the intersection of MLOps and modern GenAI systems.

This certification is designed for ML engineers, MLOps engineers, LLMOps engineers, data engineers, backend software developers, and data scientists who work with AWS-based ML and AI pipelines. The target candidate should have at least one year of hands-on experience with Amazon SageMaker AI, Amazon Bedrock, and related AWS ML services, as well as at least one year of experience in a related technical role such as backend development, DevOps, or data science.

Candidates should be comfortable with both traditional ML workflows (model training, hyperparameter tuning, deployment pipelines) and generative AI concepts (foundation models, prompt engineering, RAG, agentic workflows). This exam is not intended for individuals focused purely on ML research, data analysis, or full end-to-end solution architecture—those tasks are explicitly out of scope.

AWS does not impose formal prerequisites for the MLA-C02 exam, but the recommended experience profile is substantial. Candidates should have at least one year of practical experience using SageMaker AI and Amazon Bedrock for ML engineering tasks. General IT knowledge requirements include familiarity with common ML algorithms and their use cases, understanding of FM capabilities and limitations, data engineering fundamentals (ingestion, transformation, common formats like Parquet, JSON, ORC), software engineering best practices, CI/CD pipelines, and infrastructure as code (IaC).

On the AWS side, candidates should be proficient with SageMaker AI algorithms and features for both traditional and GenAI models, Amazon Bedrock features, AWS data storage and processing services (S3, Glue, EMR, Kinesis), AWS monitoring and logging tools, and IAM and security best practices. Prior experience with the MLA-C01 exam or foundational AWS certifications (such as AWS Certified Cloud Practitioner or AWS Certified Developer) is beneficial but not required.

The currently bookable MLA-C02 beta exam has 85 multiple-choice and multiple-response questions, a 170-minute time limit, and a $75 USD beta price. AWS explains that beta exams contain additional statistical-evaluation items that do not affect the score. The published standard MLA-C02 exam guide describes 50 scored questions plus 15 unidentified unscored questions, for 65 total, and a minimum passing score of 720 on a 100–1,000 scale. Unanswered questions are incorrect and there is no guessing penalty. AWS uses compensatory scoring, so candidates need to pass the exam overall rather than every domain separately. Beta delivery begins September 29, 2026; AWS has not yet announced general-availability registration or delivery dates. The standard Associate exam price is $150 USD.

Earning the AWS Certified Machine Learning Engineer - Associate positions candidates for roles such as ML engineer, MLOps engineer, LLMOps engineer, and data engineer at organizations deploying AI workloads on AWS. According to the World Economic Forum, demand for AI and Machine Learning Specialists is projected to grow by 40%, while 70% of North American IT leaders report difficulty filling AI/ML specialist roles—making certified practitioners especially sought after in the current hiring market.

MLA-C02's expanded coverage of generative AI, Amazon Bedrock, agentic workflows, and RAG architectures differentiates it from older ML certifications and aligns directly with enterprise priorities around deploying production GenAI systems. Professionals holding this certification can demonstrate practical competency in the full ML and GenAI engineering lifecycle—from data ingestion and model development through deployment, monitoring, cost optimization, and security—making it a strong credential for both career entry into ML engineering and advancement into senior MLOps or LLMOps roles.