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

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.

• 1.Domain 1: Data Preparation for Machine Learning (28%) — Covers ingesting data from AWS sources, transforming and validating datasets, feature engineering, handling class imbalance, splitting datasets, and using AWS services such as Amazon S3, AWS Glue, Amazon Athena, and SageMaker Data Wrangler to prepare data for ML modeling.
• 2.Domain 2: ML Model Development (26%) — Covers selecting appropriate ML algorithms and frameworks, training models using Amazon SageMaker, tuning hyperparameters with SageMaker Automatic Model Tuning, analyzing model performance metrics, managing model versions with the SageMaker Model Registry, and applying responsible AI practices.
• 3.Domain 3: Deployment and Orchestration of ML Workflows (22%) — Covers selecting deployment infrastructure and endpoint types (real-time, batch, asynchronous), provisioning and managing compute resources, configuring auto scaling, building and automating CI/CD pipelines for ML using services like AWS CodePipeline, AWS Step Functions, and Amazon SageMaker Pipelines.
• 4.Domain 4: ML Solution Monitoring, Maintenance, and Security (24%) — Covers monitoring model performance and data drift using SageMaker Model Monitor, setting up logging and alerting with Amazon CloudWatch, retraining triggers, applying security controls such as IAM policies, VPC configurations, encryption at rest and in transit, and ensuring compliance and auditability of ML systems.

• Start with the official AWS Exam Guide (available on the AWS Certification page) to understand the four domains and their weights—use it to identify your weakest areas and prioritize study time accordingly.
• Complete the official 'Exam Prep Enhanced Course: AWS Certified Machine Learning Engineer – Associate' on AWS Skill Builder, which includes exam-style questions with instructor walkthroughs and maps directly to the four exam domains.
• Get hands-on with Amazon SageMaker end-to-end: practice building training jobs, creating endpoints, configuring SageMaker Pipelines, and setting up Model Monitor. The exam heavily emphasizes operational SageMaker knowledge over theory.
• Use AWS Builder Labs and AWS Cloud Quest (available on AWS Skill Builder) to practice in real AWS environments without incurring account costs—focus on labs covering SageMaker, Glue, Step Functions, and CloudWatch.
• Take the official AWS practice exam (available on AWS Skill Builder) under timed conditions to acclimate to the ordering and matching question formats, which are less familiar than standard multiple choice.
• Study the in-scope AWS services list published in the official exam guide—pay particular attention to SageMaker features (Data Wrangler, Feature Store, Model Registry, Pipelines, Model Monitor) and supporting services like AWS Glue, Amazon Athena, AWS CodePipeline, and Amazon CloudWatch.
• Review the out-of-scope topics in the official exam guide to avoid over-studying—deep NLP/computer vision domain work, full solution architecture, and model quantization are explicitly not tested, so focus your energy on MLOps and deployment workflows.

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.