AWS Certified Machine Learning Engineer - Associate (MLA-C01) Practice Tests

AWS is retiring MLA-C01 on September 28, 2026, replacing it with MLA-C02. If you're reading this before the cutoff, MLA-C01 is still the live, bookable exam, and everything below reflects it. If you're past that date, MLA-C01 can no longer be scheduled; check AWS's certification page for MLA-C02's current details before you start studying.

While it is still active, MLA-C01 weights Data Preparation for Machine Learning heaviest at 28 percent of scored content, followed by ML Model Development at 26 percent, ML Solution Monitoring, Maintenance, and Security at 24 percent, and Deployment and Orchestration of ML Workflows at 22 percent. This practice bank of 582 questions is built to match that split, so data-pipeline and feature-engineering scenarios get real depth instead of an even split across four domains.

On test day you face 65 questions in 130 minutes: 50 scored and 15 unscored questions AWS uses to evaluate future content. Scoring is scaled from 100 to 1,000, and you need 720 to pass. AWS recommends at least 1 year of experience using Amazon SageMaker and other AWS ML services, plus 1 or more years in a related role such as backend developer, DevOps engineer, data engineer, or data scientist. The exam costs $150 and the certification is valid for 3 years. Start with the 30 free questions, then work through the full question bank until your accuracy holds steady across all four domains, before the September 28 cutoff.

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) validates a candidate's ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. Launched in October 2024, this role-based certification fills a critical gap in the AWS certification portfolio, targeting the engineer who bridges the gap between data science prototypes and production-ready ML systems. The exam tests practical knowledge of the full ML engineering lifecycle: data ingestion and preparation, model training and tuning, deployment and orchestration, and ongoing monitoring and security.

The certification covers a broad set of AWS services centered on Amazon SageMaker, alongside data storage and processing services, CI/CD and orchestration tools, monitoring and logging platforms, and security controls. Notably, the exam does not test deep domain expertise in NLP or computer vision, nor does it cover end-to-end ML solution architecture—those concerns fall to the AWS Certified Machine Learning – Specialty exam. MLA-C01 is specifically scoped to the operational and engineering tasks an ML engineer performs day-to-day in a cloud environment.

This certification is designed for ML engineers, MLOps engineers, DevOps engineers, data engineers, and backend software developers who work with machine learning systems on AWS. The ideal candidate has at least one year of hands-on experience with Amazon SageMaker and related AWS ML services, combined with at least one year of experience in a related engineering role. Data scientists looking to strengthen their deployment and operationalization skills will also benefit.

Candidates should be comfortable with software engineering best practices such as modular code design, debugging, and deployment, as well as CI/CD pipelines, Infrastructure as Code (IaC), and version control. This is not an entry-level credential—it assumes working knowledge of ML concepts, data engineering fundamentals, and cloud infrastructure provisioning.

There are no mandatory prerequisites to sit for the MLA-C01 exam; AWS does not require any prior certification. However, AWS recommends at least one year of hands-on experience using Amazon SageMaker and other AWS ML engineering services, as well as one year of experience in a related role such as backend development, DevOps, or data engineering.

Recommended foundational knowledge includes: common ML algorithms and their use cases, data engineering concepts (formats, ingestion pipelines, transformation), data querying and transformation skills, CI/CD pipeline design and orchestration, cloud resource provisioning and monitoring, and AWS security fundamentals including identity management and encryption. Candidates new to AWS may benefit from first earning the AWS Certified Cloud Practitioner or AWS Certified AI Practitioner, though neither is required.

The MLA-C01 exam consists of 65 total questions—50 scored and 15 unscored. The unscored questions are used by AWS to evaluate potential future exam content and are not identified during the exam. The time limit is 130 minutes. The exam is delivered through Pearson VUE at a testing center or via online proctoring. It is available in English, Japanese, Korean, and Simplified Chinese. The exam fee is $150 USD.

Four question types are used: multiple choice (one correct answer out of four), multiple response (two or more correct answers out of five or more options, requiring all correct selections for credit), ordering (arrange 3–5 steps in the correct sequence), and matching (match 3–7 response pairs). Scores are reported on a scaled range of 100–1,000, with a passing score of 720. The exam uses a compensatory scoring model, meaning no per-domain minimum is required. Unanswered questions are scored as incorrect; there is no penalty for guessing. The certification is valid for three years.

The MLA-C01 certification targets some of the fastest-growing roles in technology. The World Economic Forum's Future of Jobs Report projects demand for AI and ML Specialists to grow by more than 80% by 2030, and the U.S. Bureau of Labor Statistics forecasts 34% growth for data scientists between 2024 and 2034. The certification validates skills directly applicable to ML Engineer, MLOps Engineer, and AI/ML Platform Engineer roles. While the credential launched in late 2024 and direct salary correlation data is still emerging, related benchmarks are strong: ZipRecruiter places AWS Machine Learning Engineers at an average of approximately $145,000–$146,000 annually, and Payscale data shows that ML engineers with AWS skills earn roughly $5,600 more than peers without them. AWS Certified Machine Learning – Specialty holders average $213,000 according to Skillsoft's 2024 IT Skills and Salary Report, indicating the premium that AWS ML credentials command.

Within the AWS certification ecosystem, MLA-C01 sits between the AWS Certified AI Practitioner (foundational) and the AWS Certified Machine Learning – Specialty (advanced), making it a natural stepping stone for engineers building a structured AWS ML career path. It complements the AWS Certified Data Engineer – Associate and AWS Certified Solutions Architect – Associate, and is increasingly listed as a preferred qualification in ML engineering job postings on major platforms.