AWS Certified Machine Learning - Specialty (MLS-C01) Practice Tests

Use this MLS-C01 practice exam to prepare for AWS Certified Machine Learning - Specialty (MLS-C01) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 860 questions for AWS MLS-C01, 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 Engineering, Exploratory Data Analysis, Modeling, and Machine Learning Implementation and Operations. 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 – Specialty (MLS-C01) is an advanced-level certification that validates a candidate's ability to design, build, train, tune, deploy, optimize, and maintain machine learning solutions on the AWS Cloud. It tests deep knowledge across the full ML lifecycle—from raw data ingestion and pipeline construction through exploratory analysis, algorithm selection, model development, and production-grade operationalization. The exam spans four content domains weighted by importance: Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation and Operations (20%), with particular emphasis on Amazon SageMaker and complementary AWS services such as S3, EC2, IAM, AWS Glue, and Amazon Rekognition. Note: AWS has announced this certification will be retired on March 31, 2026. Candidates who earn it will retain an active certification for three years from their exam date. AWS recommends the AWS Certified Machine Learning Engineer – Associate as the forward-looking replacement credential.

This certification is designed for professionals performing AI/ML development or data science roles who have substantial hands-on experience architecting and running ML workloads in production on AWS. Ideal candidates include machine learning engineers, data scientists, MLOps engineers, and solutions architects who regularly work with model training pipelines, feature engineering workflows, and model deployment infrastructure. AWS recommends at least two years of experience developing, architecting, and running ML and deep learning workloads on the AWS Cloud before attempting this exam. Candidates are expected to understand ML algorithms conceptually and be comfortable selecting appropriate approaches for specific business problems without needing to derive mathematical proofs or build algorithms from scratch.

There are no mandatory prerequisites to register for MLS-C01; however, AWS strongly recommends that candidates have a minimum of two years of hands-on experience with ML and deep learning workloads on AWS. Practically, successful candidates typically hold one or more associate-level AWS certifications—most commonly AWS Certified Solutions Architect – Associate, AWS Certified Machine Learning Engineer – Associate, or AWS Certified Data Engineer – Associate—before attempting this Specialty exam. Candidates should be comfortable with common ML frameworks (TensorFlow, PyTorch, scikit-learn), core statistical concepts, data preprocessing techniques, hyperparameter tuning fundamentals, and standard AWS infrastructure services. Topics explicitly out of scope include extensive custom algorithm development, advanced mathematical proofs, complex DevOps or networking configurations, and advanced EMR cluster management.

The MLS-C01 exam consists of 65 total questions, of which 50 are scored and 15 are unscored pretest questions used for future exam development; candidates cannot identify which questions are unscored. Question types include multiple-choice (one correct answer from four options) and multiple-response (two or more correct answers from five or more options). The time limit is 180 minutes (3 hours). The exam is delivered either at a Pearson VUE authorized testing center or via online proctoring from a private location with webcam access and a stable internet connection. Results are reported on a scaled score from 100 to 1,000, with a minimum passing score of 750. The exam uses a compensatory scoring model—no minimum score is required in any individual domain; only the total scaled score determines pass/fail. No penalty is applied for guessing; unanswered questions are treated as incorrect. The exam fee is $300 USD and is available in English, Japanese, Korean, and Simplified Chinese.

Holders of the AWS Certified Machine Learning – Specialty credential are positioned for senior ML engineering, data science, and MLOps roles in organizations running production AI workloads on AWS. According to Skillsoft's 2024 IT Skills and Salary Report, the average salary for MLS-C01 holders reaches $213,267 at senior levels, reflecting the credential's recognition as an advanced, specialist qualification. Mid-career professionals (5–9 years) with this certification average around $157,460, with experienced engineers frequently exceeding $193,000 annually. The World Economic Forum's Future of Jobs Report projects demand for AI and ML specialists will grow more than 80% by 2030, underpinning strong long-term market value for this credential. As AWS retires MLS-C01 in March 2026, current holders gain a competitive differentiation window; the AWS Certified Machine Learning Engineer – Associate is the recommended next credential for professionals who want to maintain AWS-validated ML expertise after the retirement date.