AWS Certified Machine LearningSpecialty (MLS-C01)
Validate your expertise in building, training, tuning, and deploying machine learning models on AWS. This advanced certification demonstrates your ability to design and implement ML solutions using AWS services for data scientists and ML engineers.
Exam Duration
180 minutes
Exam Cost
$300 USD
Question Format
65 Questions
Exam Content Breakdown
Data Engineering
Exploratory Data Analysis
Modeling
Machine Learning Implementation and Operations
Key AWS ML Services
Amazon SageMaker
ML Platform
AWS Glue
ETL
Amazon Kinesis Data Streams
Streaming
Amazon Kinesis Data Firehose
Data Delivery
Amazon S3
Storage
Amazon EMR
Big Data
AWS Batch
Compute
Amazon DynamoDB
Database
Amazon RDS
Database
Amazon Redshift
Data Warehouse
Amazon CloudWatch
Monitoring
AWS IAM
Security
Amazon VPC
Networking
AWS KMS
Security
Amazon API Gateway
API Management
AWS Lambda
Serverless
Amazon EC2
Compute
Amazon ECR
Container Registry
Amazon ECS
Container Service
AWS Step Functions
Orchestration
Study with Zertly
ML Algorithm Mastery
Deep dive into SageMaker built-in algorithms and when to use each for different ML problems.
Data Engineering Focus
Master data pipelines, feature engineering, and ETL processes for ML workflows.
Model Optimization
Learn hyperparameter tuning, model evaluation, and performance optimization techniques.
MLOps & Deployment
Understand model deployment, monitoring, and operational best practices in production.
SageMaker Built-in Algorithms & ML Concepts
Understand when and how to use these algorithms and ML techniques in your solutions.
Linear Regression
Supervised
Logistic Regression
Supervised
XGBoost
Ensemble
Random Forest
Ensemble
K-Means Clustering
Unsupervised
K-Nearest Neighbors
Supervised
Neural Topic Model
NLP
Latent Dirichlet Allocation
NLP
Principal Component Analysis
Dimensionality Reduction
Factorization Machines
Recommendation
Image Classification
Computer Vision
Object Detection
Computer Vision
Semantic Segmentation
Computer Vision
Text Classification
NLP
Sequence-to-Sequence
NLP
BlazingText
NLP
Expert Preparation Tips
Proven strategies to maximize your ML expertise and exam success rate.
Study Resources
- Hands-on experience with SageMaker Studio and Jupyter notebooks
- Practice with AWS ML services using sample datasets
- Study machine learning theory and statistical concepts
- Zertly's AI-generated practice questions and mock exams
Exam Day Strategies
- Focus on scenario-based questions - think about real-world ML projects
- Remember the ML workflow: data preparation → modeling → deployment
- Consider cost optimization and performance trade-offs
- Understand when to use different SageMaker algorithms and instance types
Frequently Asked Questions
Get answers to the most common questions about the AWS Machine Learning Specialty certification.
How long is the AWS Machine Learning Specialty certification valid?
The AWS Machine Learning Specialty certification is valid for 3 years from the date of certification. You can recertify by passing the current version of the exam or by completing continuing education activities.
What prerequisites are required for the MLS-C01 exam?
AWS recommends having at least 2 years of experience developing, architecting, or running machine learning/deep learning workloads on the AWS Cloud. Familiarity with basic ML concepts, statistics, and programming (Python recommended) is essential.
What is the exam format for AWS MLS-C01?
The MLS-C01 exam consists of 65 questions (multiple-choice and multi-answer) with 180 minutes (3 hours) to complete. The exam uses a pass/fail scoring system rather than a numerical score.
Which programming languages should I know for this exam?
While the exam does not test programming directly, familiarity with Python is highly recommended as it is the primary language used with AWS ML services. Knowledge of SQL for data querying and basic understanding of R or Scala can also be helpful.
How does Zertly help me prepare for the AWS ML Specialty exam?
Zertly provides AI-generated practice questions covering all four exam domains, detailed explanations with real-world scenarios, progress tracking across ML topics, and personalized study recommendations based on your performance.
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