AWS · MLS-C01
AWS Certified Machine Learning - Specialty (MLS-C01) Practice Tests
Validates ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for business problems using AWS Cloud.
Exam Details
Practice Questions
860
≈ 13 practice exams
Duration
180 minutes
Passing Score
750/1000
Difficulty
SpecialtyLast Updated
Oct 2026
Topics Covered
Data EngineeringExploratory Data AnalysisModelingMachine Learning Implementation and Operations
AWS Machine Learning Specialty MLS-C01 Legacy Guide
AWS retired the Machine Learning - Specialty certification: the last day to take MLS-C01 was March 31, 2026. People who already hold it keep the credential until its original three-year expiry. AWS points to its ML Engineer Associate, AI Practitioner, Data Engineer Associate, and Generative AI Developer Professional certifications as the current paths, with no single one-to-one replacement.
The retired exam had 65 questions (50 scored and 15 unscored) in 180 minutes, cost $300, and required 750 on a 100-1,000 scale. Its four domains were Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation and Operations (20%), so it leaned heavily on model selection and tuning.
Use these 860 questions as a way to study machine learning concepts and SageMaker-era workflows, not to prepare for a live booking. If you are choosing a new exam, compare the MLA-C02 and Generative AI Developer Professional pages, which cover the engineering and generative AI work AWS now certifies.
Exam Domain Breakdown
Data Engineering20%
Exploratory Data Analysis24%
Modeling36%
Machine Learning Implementation and Operations20%
Exam Overview
The AWS Certified Machine Learning - Specialty (MLS-C01) exam has been retired; the last day to take it was March 31, 2026. People who already hold the credential keep it until its original three-year expiry. AWS points to its ML Engineer Associate, AI Practitioner, Data Engineer Associate, and Generative AI Developer Professional certifications as current paths.
The retired exam covered Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation and Operations (20%).
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
MLS-C01 had 65 questions (50 scored and 15 unscored) in 180 minutes, cost $300, and required 750 on a 100-1,000 scale. It can no longer be scheduled.
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
The credential remains valid for existing holders until it expires. For a new certification, AWS's current machine learning engineering and generative AI exams are the better target.
Sample Questions
5 sample questions with answers and explanations. The full bank has 860 questions, enough for 13 full-length practice exams.
Preview — answers shown1. VanArsdel Ltd is visualizing relationships between customer age and purchase frequency. Which method suits this?
APie chart
BScatter plot
CLine chart
DBar chart
Explanation
Scatter plots display relationships between two numerical variables like age and frequency. Line charts show trends over time. Pie charts represent parts of a whole. Bar charts compare categories.
2. An organization sets cost quotas and alerts for SageMaker expenses. Which service do they use?
AAWS Cost Explorer
BAWS Trusted Advisor
CAWS Budgets
DAmazon CloudWatch
Explanation
AWS Budgets sets quotas and alerts when usage exceeds limits. AWS Cost Explorer provides insights into spending trends. AWS Trusted Advisor offers cost optimization recommendations. Amazon CloudWatch monitors performance, not costs.
3. Litware Inc. is using AWS SageMaker to handle an imbalanced dataset for fraud detection. Which performance metric is most appropriate?
ARecall
BAUC
CPrecision
DF1 Score
Explanation
AUC is effective for imbalanced classes as it evaluates the model's ability to rank positive instances higher than negative ones.
4. Alpine Ski House aims to improve model performance by sequentially correcting errors from previous models. Which ensemble technique fits this?
AStacking
BBagging
CEnsembling
DBoosting
Explanation
Boosting builds models sequentially, each correcting errors of the prior one, enhancing overall performance. Ensembling combines predictions generally. Stacking uses a meta-model for outputs. Bagging averages from resampled subsets to reduce variance.
5. Fourth Coffee needs to visualize data distributions for exploratory analysis. Which plot is most suitable for categorical variables?
APie chart.
BHistogram.
CScatter plot.
DLine chart.
Explanation
Pie charts show proportions of categorical data. Scatter plots for relationships, histograms for numerical distributions, line charts for trends over time.
Frequently Asked Questions
Can I still take the AWS Machine Learning Specialty exam?
No. AWS set March 31, 2026 as the last day to take MLS-C01.
Is my MLS-C01 certification still valid?
Yes. AWS says holders keep the credential until its original three-year expiry.
What replaces MLS-C01?
AWS names the ML Engineer Associate, AI Practitioner, Data Engineer Associate, and Generative AI Developer Professional certifications. There is no single one-to-one replacement.
What was the MLS-C01 exam format?
Sixty-five questions (50 scored and 15 unscored) in 180 minutes, with a 750 out of 1,000 pass mark and a $300 fee.
What were the MLS-C01 domains?
Data Engineering 20%, Exploratory Data Analysis 24%, Modeling 36%, and Machine Learning Implementation and Operations 20%.
Is MLS-C01 study material still useful?
It is useful for machine learning concepts and SageMaker-based workflows, but it does not cover the generative AI and engineering topics in AWS's current certifications.
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