AWS Certified Machine Learning - Specialty Quick Facts (2025)

AWS Certified Machine Learning - Specialty (MLS-C01) Exam Overview

Struggling to understand what it takes to pass the AWS Certified Machine Learning – Specialty exam? This complete exam overview outlines everything you need to prepare effectively, from prerequisites to domains, study strategies, scoring, and more.

What Is the AWS Certified Machine Learning - Specialty Certification?

The AWS Certified Machine Learning – Specialty certification validates your real-world skills in building, training, tuning, and deploying machine learning (ML) models in the AWS Cloud. It’s ideal for professionals involved in data science or AI/ML solution development using AWS technologies. Passing this certification demonstrates that you can design scalable, production-ready ML solutions that follow AWS best practices and drive business impact in real-world cloud environments.

Who Is This Certification For?

This certification is designed for:

• Machine Learning Engineers
• Data Scientists
• Applied AI Specialists
• Data Engineers with ML responsibilities
• Developers involved in ML-powered applications
• Cloud Architects specializing in AI/ML workloads

You should have a minimum of 2 years of experience designing, training, and optimizing ML or deep learning models in AWS before attempting this exam.

What Jobs Can I Get with This Certification?

This certification is recognized globally and aligns with roles such as:

• AWS Machine Learning Engineer
• AI/ML Specialist
• Data Scientist (AWS Stack)
• Applied Data Engineer
• AI Cloud Architect
• ML Ops Engineer
• Deep Learning Engineer
• And More AI/ML-Focused Cloud Roles...

The increasing demand for cloud-based AI and ML professionals means this certification helps you stand out in a growing job market.

What Exam Code Should I Take?

The current exam code is MLS-C01. There is only one version of the exam at this time.

How Much Does the Exam Cost?

The AWS Certified Machine Learning – Specialty exam costs $300 USD. Pricing may vary by country or currency. AWS also offers a 50% discount on your next exam if you already hold an active AWS Certification.

How Many Questions Are on the Exam?

The exam consists of 65 questions, a combination of multiple choice and multiple response formats. Note that 15 of these questions are unscored and without identification—they are used for future exam calibration.

How Much Time Do You Have for the Exam?

Candidates have 180 minutes (3 hours) to complete the exam.

What Languages Is the Exam Available In?

The exam is offered in:

• English
• Japanese
• Korean
• Simplified Chinese

What's the Passing Score?

The passing mark is 750 on a scale of 100–1000. AWS uses a scaled scoring model to adjust for slight variations in difficulty across exam versions.

Is the Exam Hard?

Yes, the AWS Certified Machine Learning - Specialty exam is a challenging, expert-level certification. It tests not only your ML knowledge, but your ability to apply that knowledge using AWS services to solve real-world problems—at scale, and securely. Success requires deep understanding across modeling techniques, data engineering, optimization, deployment, and operations.

Candidates consistently report that hands-on AWS experience, deep understanding of ML workflows, and using top-tier practice exams are the keys to a strong performance. We highly recommend investing time into realistic, professional AWS Machine Learning Specialty practice exams to test your readiness and build confidence.

What Domains Does the Exam Cover and What Are Their Weightings?

The MLS-C01 exam divides its content across four major domains:

1. Modeling (36%)

   • Framing business problems as ML problems
   • Selecting the correct algorithms and models
   • Training and evaluating models
   • Hyperparameter optimization
   • Bias, variance, and model comparison techniques

2. Exploratory Data Analysis (24%)

   • Data cleansing and labeling
   • Feature engineering including tokenization, one-hot encoding, and dimensionality reduction
   • Visualization techniques and statistical analysis

3. Data Engineering (20%)

   • Data ingestion (batch and streaming)
   • Data transformation using AWS Glue, EMR, or Spark
   • Setting up data repositories (S3, EFS, etc.)

4. Machine Learning Implementation & Operations (20%)

   • Model deployment and scaling with SageMaker and related services
   • Performance tuning, resiliency, and cost optimization
   • Security best practices (IAM, encryption, VPCs)
   • Monitoring, A/B testing, rollback strategies

Are There Any Prerequisites?

There are no formal prerequisites to take the exam, but it is recommended that you:

• Have at least 2 years of hands-on experience building ML or deep learning solutions on AWS
• Understand foundational ML concepts and algorithms
• Be familiar with common ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Know AWS ML services (e.g., Amazon SageMaker, Comprehend, Polly, Rekognition)

Most successful candidates have previously earned an AWS Associate or Professional certification (such as the Solutions Architect - Associate), but it's not required.

What Knowledge Areas Should I Focus On?

Key focus areas include:

1. AWS ML Services

   • Fully-managed ML services (SageMaker, Rekognition, Comprehend)
   • Model hosting, pipelines, built-in algorithms

2. Model Training and Optimization

   • Cross-validation, hyperparameter tuning, loss functions, GPU usage
   • Ensemble learning, transfer learning, LLM concepts

3. Data Management

   • Streaming via Kinesis or Flink
   • Batch ingestion and transformation
   • Feature stores and ETL orchestration

4. Security and Cost Optimization

   • IAM roles, bucket-level access
   • Spot vs. On-Demand training
   • Encryption, compliance considerations

5. Monitoring & Operations

   • Model monitoring, drift detection, S3 triggers
   • Troubleshooting failed deployments
   • Integration with CloudWatch, Lambda, and CloudTrail

Common Mistakes to Avoid

Avoid these pitfalls:

• Underestimating the exam's technical complexity—many candidates fail due to lack of real-world AWS ML experience
• Skipping hands-on practice labs, especially those involving SageMaker and Glue
• Forgetting to study deployments and operations, not just modeling concepts
• Confusing algorithms or metrics—AUC, F1, RMSE have distinct use cases
• Not testing yourself against realistic practice exams—simulate the real test pace and question format using expertly developed AWS Certified Machine Learning - Specialty practice exams

How Can I Prepare for the Exam?

Use these resources for guided preparation:

1. AWS Skill Builder and Exam Guide

   • Follow the official Exam Prep Plan on AWS Skill Builder
   • Understand each domain in the AWS Certified Machine Learning - Specialty Exam Guide

2. Official Practice Questions

   • Take the AWS Certification Official Practice Question Set
   • Analyze each answer and rationale carefully

3. Hands-On Labs and Simulations

   • Work with SageMaker notebooks
   • Practice data transformations in Glue
   • Deploy and monitor models in different configurations

4. Courses and Bootcamps

   • Join AWS Training or high-quality third-party courses
   • Watch walkthroughs with real AWS console sessions

5. Documentation and Whitepapers

   • Review AWS documentation for ML services covered in the exam
   • Study customer use cases and architectural patterns

6. Practice Tests

   • Time yourself with full-length mock exams
   • Identify weak domains early
   • Focus on comprehension, not memorization

How Long Is the Certification Valid?

The AWS Certified Machine Learning – Specialty Certification is valid for 3 years. You can recertify by retaking the latest version of the MLS-C01 exam before your certificate expires.

What Should I Take After the Machine Learning Specialty?

If you want to expand your cloud ML skills even further, consider:

• AWS Certified Data Engineer – Associate
• AWS Certified Solutions Architect – Professional
• Specialized AI/ML tools across frameworks such as Hugging Face, SageMaker Studio Labs, and Amazon Bedrock

These can help you transition into more senior technical roles and architect advanced ML pipelines at scale.

Where Can I Take the Exam?

The exam is available through:

1. Pearson VUE Online Proctoring

   • Take the exam from home in a secure environment
   • Must have webcam, microphone, and stable internet

2. Testing Centers

   • Certified Pearson VUE locations worldwide
   • Live proctoring environment

How Do I Register for the Exam?

Follow these steps to register:

1. Go to the AWS Certified Machine Learning - Specialty official page
2. Click “Schedule Exam”
3. Choose Pearson VUE as your testing provider
4. Select date and time (either online or in-person)
5. Pay the exam fee and receive your confirmation

What If I Don’t Pass the Exam?

Should you fail:

• Review performance by domain to target weaknesses
• Wait 14 days before retaking
• Use AWS-provided feedback to improve
• Reinforce practice with additional labs and realistic exam simulations

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Prepare intelligently. Study strategically. And remember—real-world AWS ML experience and thoughtful practice are the keys to earning your AWS Certified Machine Learning – Specialty certification. Good luck!