AWS Certified Machine Learning Specialty (MLS-C01): What It Is and When to Use It

IMPORTANT NOTE: The AWS Certified Machine Learning - Specialty (MLS-C01) certification is being retired. The last day to take this exam is March 31, 2026. For individuals starting their machine learning certification journey on AWS, the newer AWS Certified Machine Learning Engineer - Associate is the recommended path.

Definition

The AWS Certified Machine Learning - Specialty (MLS-C01) is a high-level certification that validates an individual's expertise in designing, building, training, tuning, and deploying machine learning (ML) models on the AWS Cloud. It is designed for professionals in data science or development roles who have extensive experience with the entire ML lifecycle, from data engineering and exploratory analysis to model operationalization.

Exam Format and Structure

The MLS-C01 exam assesses a candidate's ability to apply ML concepts and AWS services to solve complex business problems. The exam format is as follows:

Exam Code: MLS-C01
Duration: 180 minutes.
Question Count: 65 questions. This includes 15 unscored questions that AWS uses for statistical evaluation and do not affect the final score.
Question Types: The exam consists of two types of questions: multiple-choice (one correct answer) and multiple-response (two or more correct answers).
Scoring: Results are reported as a scaled score between 100 and 1,000. The minimum passing score is 750. AWS uses a compensatory scoring model, meaning the overall score determines the pass/fail result, and you do not need to pass each domain individually.
Retirement Date: The last day to take the exam is March 31, 2026.

Who Should Take This Exam?

The ideal candidate for the MLS-C01 certification is an individual with deep, hands-on experience in machine learning on the AWS platform. AWS recommends the following prerequisites:

Experience: At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud.
Role: Data scientists, ML developers, and data engineers who are responsible for the end-to-end ML pipeline.
Knowledge: A strong understanding of basic ML algorithms, experience with hyperparameter optimization, familiarity with ML and deep learning frameworks (like TensorFlow, PyTorch, or MXNet), and knowledge of model training and deployment best practices.

While not mandatory, holding an Associate-level or Professional-level AWS certification is beneficial before attempting a Specialty exam.

Pricing Model

The registration fee for the AWS Certified Machine Learning - Specialty exam is 300 USD, plus any applicable taxes. Candidates who have passed a previous AWS certification exam receive a 50% discount voucher in their AWS Certification Account, which can be applied to this exam fee.

Pros and Cons

Pros:

Validates Deep Expertise: Passing this exam is a significant achievement that demonstrates a comprehensive understanding of the entire ML lifecycle on AWS, from data ingestion to model deployment and monitoring.
High Industry Recognition: The MLS-C01 is widely regarded as one of the most challenging and valuable AWS certifications, often associated with high-paying job roles.
Comprehensive Scope: The exam covers four critical domains—Data Engineering, Exploratory Data Analysis, Modeling, and Operations—proving a well-rounded skill set.

Cons:

Retiring Certification: The primary drawback is its retirement on March 31, 2026. New candidates may prefer to pursue the newer, role-based AWS Certified Machine Learning Engineer - Associate certification for a longer-term credential.
High Difficulty: The exam is notoriously difficult, requiring not just theoretical knowledge but significant practical, hands-on experience with AWS services and ML concepts.
Broad Knowledge Required: The scope is extensive, requiring proficiency in data services (like Amazon Kinesis, AWS Glue), core ML services (Amazon SageMaker), and operational tools (Amazon CloudWatch).

Comparison with Alternatives

AWS Certified Machine Learning - Specialty (MLS-C01) vs. AWS Certified Machine Learning Engineer - Associate (MLE-A)

The most direct alternative is the newer AWS Certified Machine Learning Engineer - Associate. The key differences are in their focus, target audience, and experience level:

Target Role: MLS-C01 is broader, targeting data scientists and ML specialists responsible for the entire lifecycle. MLE-A is more focused on the engineering and operational aspects, targeting ML Engineers, MLOps Engineers, and DevOps Engineers who productionize models.
Experience Level: MLS-C01 recommends 2+ years of experience with ML workloads on AWS. MLE-A is designed for individuals with at least 1 year of experience.
Exam Focus: MLS-C01 dedicates significant weight to Data Engineering (20%) and Exploratory Data Analysis (24%). The MLE-A exam places a heavier emphasis on implementing and operationalizing ML workloads, focusing more on the post-modeling stages.
Future Path: As MLS-C01 is retiring, the MLE-A represents the current and future certification path for ML professionals on AWS.

Exam Domains

The MLS-C01 exam content is divided into four domains, each with a specific weighting:

Domain 1: Data Engineering (20%)

This domain focuses on creating data pipelines to ingest, store, and transform data for machine learning. Key topics include:

Data Repositories: Using Amazon S3 for durable, scalable data lakes.
Ingestion Solutions: Implementing solutions for batch and streaming data using services like Amazon Kinesis (Data Streams, Data Firehose) and AWS Glue.
Transformation Solutions: Performing Extract, Transform, Load (ETL) operations using AWS Glue, Amazon EMR, and Amazon SageMaker Processing jobs to prepare data for model training.

Domain 2: Exploratory Data Analysis (24%)

This domain covers the techniques used to understand, clean, and prepare data before modeling. It tests knowledge of:

Data Sanitization and Preparation: Handling missing data, outliers, and inconsistent data types.
Feature Engineering: Creating and selecting relevant features to improve model performance, including techniques like one-hot encoding, binning, and normalization.
Data Analysis and Visualization: Using tools like Amazon SageMaker Data Wrangler, Amazon Athena for querying data in S3, and Amazon QuickSight for visualization to gain insights from the data.

Domain 3: Modeling (36%)

As the most heavily weighted domain, this section covers the core of the machine learning process. Candidates must demonstrate proficiency in:

Problem Framing: Translating business problems into ML problems (e.g., classification, regression, forecasting).
Model Selection: Choosing appropriate models and algorithms, including Amazon SageMaker's built-in algorithms, for a given problem.
Model Training: Configuring and running training jobs efficiently using Amazon SageMaker, including understanding concepts like distributed training.
Hyperparameter Optimization: Using Amazon SageMaker's automatic hyperparameter tuning capabilities to find the best model parameters.
Model Evaluation: Interpreting key metrics (AUC-ROC, F1-score, RMSE), analyzing confusion matrices, and detecting issues like overfitting, underfitting, and model bias.

Domain 4: Machine Learning Implementation and Operations (20%)

This domain focuses on deploying and managing ML models in a production environment (MLOps). Key areas include:

Deployment: Building scalable and resilient solutions by deploying models to Amazon SageMaker endpoints (real-time, serverless, asynchronous) or using AWS Batch for batch predictions.
Security: Applying AWS security best practices, including using AWS IAM roles, encrypting data at rest and in transit, and securing network access with VPCs.
Operationalization: Building and automating ML workflows using tools like Amazon SageMaker Pipelines, monitoring model performance with Amazon CloudWatch, and implementing strategies for model retraining.

Frequently Asked Questions

Q: What is the passing score for the MLS-C01 exam?

A: The minimum passing score is 750 on a scaled range of 100 to 1,000.

Q: Is programming or advanced math required to pass?

A: No. The exam does not require you to write complex algorithms from scratch or perform advanced mathematical proofs. It focuses on the applied knowledge of selecting, training, deploying, and managing ML solutions using AWS services. A conceptual understanding of ML algorithms and proficiency in interpreting their results is far more important.

Q: Since the MLS-C01 is retiring, is it still worth taking?

A: It depends on your goals. If you are an experienced ML professional who wants to validate deep, existing expertise before the March 31, 2026 deadline, it is a valuable credential that remains active for three years from the date it is earned. However, if you are just beginning your AWS ML journey, the AWS Certified Machine Learning Engineer - Associate is a more strategic, long-term choice as it represents the current certification path.

This article reflects AWS features and pricing as of 2026. AWS services evolve rapidly — always verify against the official AWS documentation before making production decisions.