AWS · MLS-C01
AWS Certified Machine Learning - Specialty (MLS-C01) Practice Test
Validates ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for business problems using AWS Cloud.
Exam Details
Questions
860
Duration
180 minutes
Passing Score
750/1000
Difficulty
SpecialtyLast Updated
Jan 2025
Topics Covered
Data EngineeringExploratory Data AnalysisModelingMachine Learning Implementation and Operations
AWS Certified Machine Learning - Specialty (MLS-C01) Practice Exam Preparation
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.
Exam Domain Breakdown
Data Engineering20%
Exploratory Data Analysis24%
Modeling36%
Machine Learning Implementation and Operations20%
Exam Overview
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.
Who Should Take This Exam
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.
Prerequisites
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.
Exam Format
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.
Skills Measured
- 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.
Study Tips
- 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.
Career Benefits
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.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 860 questions.
Preview — answers shown1. Northwind Traders is exploring their review dataset and wants to visualize the distribution of star ratings using a bar plot. Which library and method should they use to create this visualization?
ARun df.duplicated() to count duplicates and visualize with a histogram.
BApply Seaborn sns.barplot with x as rating indices and y as counts from value_counts().
CUse scipy.sparse to create a matrix and plot the sparsity pattern.
DUse Pandas df.describe() to get numerical summaries and plot with Matplotlib.
Explanation
Seaborn sns.barplot takes rating indices from value_counts() as x and counts as y to visualize star rating distributions. Pandas df.describe() gives summaries but requires additional plotting libraries. df.duplicated() identifies duplicates, not distributions. scipy.sparse creates matrices for computation, not visualization.
2. Bellows College is implementing reinforcement learning for text summarization using SageMaker. They need to ensure the agent explores all possible states and defines rewards for actions. Which considerations are crucial for setting up the RL problem? (Select three!)
Multiple correct answersAGenerate negative samples randomly from IP addresses for training.
BCreate a simulation close to the real-world text environment.
CTune vector dimension for embedding text tokens.
DDefine clear rewards and penalties for summarization actions.
EUse PCA to cluster text features before RL.
FValidate agent actions with past data before real-world deployment.
Explanation
A simulation mimicking the real text environment allows safe exploration and learning without real risks. Defining rewards for good summaries and penalties for poor ones guides the agent toward optimal policies. Validating with past data ensures reliability before live use. Negative samples from IPs are for anomaly detection, not text RL. Vector dimension is for embeddings in models like IP Insights. PCA clusters features, not directly for RL setup.
3. Humongous Insurance evaluates classification models for fraud detection. Which metric provides the best comparison across models with varying thresholds?
AArea under the ROC curve
BMean absolute percentage error
CRecall
DMisclassification rate
Explanation
Area under the ROC curve evaluates model performance across thresholds, useful for binary classification comparisons. Recall focuses on true positives. Misclassification rate is basic but threshold-dependent. MAPE is for regression, not classification.
4. Humongous Insurance is building a data lake for machine learning using unstructured data. They require cost optimization and automatic scaling. Which S3 storage class should they select for data accessed unpredictably?
AS3 Intelligent Tiering
BS3 Glacier for archival
CS3 Standard for frequent use
DS3 Standard-IA for known infrequent
Explanation
S3 Intelligent Tiering automatically moves data based on access patterns, optimizing costs for unpredictable usage. S3 Standard incurs higher costs for infrequent access. S3 Standard-IA is for predictable infrequent access, not automatic. S3 Glacier is for long-term storage with retrieval delays.
5. Humongous Insurance is scaling array operations for large datasets and needs to handle matrices and linear algebra efficiently. Which package is optimized for these numerical computations and works with C/C++ for speed?
APandas for time series
BSeaborn for swarm plots
CNumPy for numerical operations
DScikit-learn for cross-validation
Explanation
NumPy is designed for linear algebra, matrices, and fast numerical operations using optimized C/C++ code. Pandas handles time series, not algebra. Seaborn makes plots, not computations. Scikit-learn validates models, not performs algebra.
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