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

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.

• 1.Domain 1 – Data Engineering (20% of scored content): Creating and maintaining data repositories for ML using services such as Amazon S3, AWS Lake Formation, and Amazon Redshift; ingesting and transforming data with AWS Glue, AWS Data Pipeline, and Amazon Kinesis; identifying and resolving data quality issues; applying feature engineering and data transformation techniques to prepare datasets for modeling.
• 2.Domain 2 – Exploratory Data Analysis (24% of scored content): Sanitizing and preparing data for modeling, including handling missing values, outliers, and class imbalances; performing feature selection and engineering; analyzing and visualizing data distributions, correlations, and statistical properties; applying dimensionality reduction techniques (PCA, t-SNE); selecting appropriate data formats and storage for downstream ML workflows.
• 3.Domain 3 – Modeling (36% of scored content): Framing business problems as ML problems and selecting appropriate algorithm families (supervised, unsupervised, reinforcement learning, deep learning); training models using Amazon SageMaker built-in algorithms and custom containers; evaluating model performance using relevant metrics (AUC-ROC, RMSE, F1 score, confusion matrices); applying hyperparameter tuning (SageMaker Automatic Model Tuning); mitigating overfitting and underfitting; selecting and applying deep learning architectures (CNNs, RNNs, Transformers) for relevant problem types.
• 4.Domain 4 – Machine Learning Implementation and Operations (20% of scored content): Building scalable and reliable ML pipelines using SageMaker Pipelines and AWS Step Functions; deploying models to SageMaker endpoints for real-time and batch inference; monitoring model performance and detecting data/model drift using SageMaker Model Monitor; applying security best practices (IAM policies, VPC configurations, encryption); managing costs and compute resources (SageMaker training instances, Spot Training); troubleshooting production ML systems and automating retraining workflows.

• Work through the official AWS Exam Prep plan available in AWS Skill Builder, which includes the exam guide, digital course content, AWS Builder Labs, and the Official Practice Question Set (65 questions with explanations) specifically mapped to MLS-C01 domains.
• Build hands-on fluency with Amazon SageMaker end-to-end: practice creating training jobs, configuring hyperparameter tuning jobs, deploying real-time endpoints, setting up batch transform jobs, and enabling SageMaker Model Monitor—this single service dominates the exam.
• Study the domain weightings and allocate preparation time accordingly: Modeling (36%) deserves the most attention, covering algorithm selection, evaluation metrics, bias-variance tradeoffs, and deep learning architectures. Spend proportionally less time on Data Engineering and MLOps domains.
• Use the official AWS Certified Machine Learning – Specialty Sample Questions and the AWS Certification Official Practice Question Set to familiarize yourself with question phrasing, especially multiple-response questions where partial knowledge leads to wrong answers.
• Review AWS whitepapers and documentation for key ML services beyond SageMaker: Amazon Rekognition, Amazon Comprehend, Amazon Forecast, Amazon Personalize, AWS Glue, and Amazon Kinesis Data Streams—the exam tests knowing which managed service is appropriate for a given scenario.
• For the Data Engineering domain, practice building end-to-end data pipelines using AWS Glue (ETL jobs, crawlers, Data Catalog) and understand when to use Kinesis Data Firehose vs. Kinesis Data Streams vs. AWS Glue Streaming for different ingestion patterns.
• Since this certification retires March 31, 2026, prioritize scheduling your exam well before that date and consider also studying for the AWS Certified Machine Learning Engineer – Associate, which serves as the primary replacement credential going forward.

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.