The AWS Certified Machine Learning – Specialty certification is designed to validate your expertise in designing, building, deploying, and maintaining machine learning (ML) solutions within the AWS ecosystem. Earning this credential showcases your ability to deliver robust and scalable ML systems that align with AWS’s architectural best practices.

– Who Should Take This Exam?

This certification is ideal for professionals working in machine learning, artificial intelligence (AI), or data science roles who leverage AWS to develop and manage ML workloads. The exam is particularly suited for individuals responsible for:

• Designing end-to-end ML solutions tailored to specific business needs
• Implementing scalable and secure ML models on AWS
• Optimizing training performance and tuning models efficiently

– Key Skills Validated

The MLS-C01 exam evaluates a candidate’s ability to:

• Choose the most suitable ML approach based on a defined business problem
• Identify and integrate the appropriate AWS services to support ML workloads
• Architect ML solutions that are cost-efficient, scalable, secure, and reliable
• Deploy and operationalize ML models in production environments
• Perform tasks such as hyperparameter tuning, model optimization, and continuous monitoring

– Target Candidate Profile

Candidates pursuing this certification are expected to have:

• At least 2 years of hands-on experience in developing or deploying ML or deep learning solutions on AWS
• A strong understanding of ML model lifecycle management, including training, validation, tuning, and operationalization

– Recommended AWS Knowledge and Skills

To succeed in the MLS-C01 exam, it is recommended that candidates possess:

• A solid grasp of the core concepts and intuition behind popular ML algorithms
• Practical experience in hyperparameter optimization and model tuning
• Familiarity with common ML and deep learning frameworks (e.g., TensorFlow, PyTorch, MXNet)
• Knowledge of AWS ML services such as Amazon SageMaker, AWS Lambda, Amazon S3, Amazon EC2, and AWS Glue
• The ability to apply best practices for training, deploying, and maintaining ML models in production
• Awareness of security, monitoring, and cost optimization strategies for ML workloads on AWS

Exam Details

• The AWS Certified Machine Learning (MLS-C01) exam falls under the Specialty category of AWS certifications and is designed to assess advanced expertise in machine learning within the AWS Cloud environment.
• Candidates are given a total of 180 minutes to complete the exam. The format consists of 65 questions, which may be either multiple choice or multiple response in nature. This structure is intended to evaluate both conceptual understanding and practical application of ML principles on AWS.
• Examinees can choose to take the test either at an authorized Pearson VUE testing center or through the online proctored exam option, offering flexibility and convenience.
• The exam is available in four languages: English, Japanese, Korean, and Simplified Chinese, catering to a global audience of AWS professionals.
• Scoring for the exam is based on a scaled score range of 100 to 1,000, with a minimum passing score of 750. This scoring model ensures fairness and consistency across different test versions.

Course Outline

The exam covers the following topics:

1. Understand Data Engineering (20%)

1.1 Creating data repositories for ML.

• Identifying data sources (for example, content and location, primary sources such as user data) (AWS Documentation: Supported data sources)
• Determining storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]). (AWS Documentation: Using Amazon S3 with Amazon ML, Creating a Datasource with Amazon Redshift Data, Using Data from an Amazon RDS Database, Host instance storage volumes, Amazon Machine Learning and Amazon Elastic File System)

1.2 Identifying and implementing a data ingestion solution.

• Identifying data job styles and job types (for example, batch load, streaming).
• Orchestrating data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
  • Amazon Kinesis (AWS Documentation: Amazon Kinesis Data Streams)
  • Amazon Data Firehose
  • Amazon EMR (AWS Documentation: Process Data Using Amazon EMR with Hadoop Streaming, Optimize downstream data processing)
  • Amazon Glue (AWS Documentation: Simplify data pipelines, AWS Glue)
  • Amazon Managed Service for Apache Flink
• Scheduling Job (AWS Documentation: Job scheduling, Time-based schedules for jobs and crawlers)

1.3 Identifying and implementing a data transformation solution.

• Transforming data transit (ETL: Glue, Amazon EMR, AWS Batch) (AWS Documentation: extract, transform, and load data for analytic processing using AWS Glue)
• Handling ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive). (AWS Documentation: Large-Scale Machine Learning with Spark on Amazon EMR, Apache Hive on Amazon EMR, Apache Spark on Amazon EMR, Use Apache Spark with Amazon SageMaker, Perform interactive data engineering and data science workflows)

2. Learn About Exploratory Data Analysis (24%)

2.1 Sanitizing and preparing data for modeling.

• Identifying and handling missing data, corrupt data, stop words, etc. (AWS Documentation: Managing missing values in your target and related datasets, Amazon SageMaker DeepAR now supports missing values, Configuring Text Analysis Schemes)
• Formatting, normalizing, augmenting, and scaling data (AWS Documentation: Understanding the Data Format for Amazon ML, Common Data Formats for Training, Data Transformations Reference, AWS Glue DataBrew, Easily train models using datasets, Visualizing Amazon SageMaker machine learning predictions)
• Determining whether there is sufficient labeled data. (AWS Documentation:data labeling for machine learning, Amazon Mechanical Turk, Use Amazon Mechanical Turk with Amazon SageMaker)
  • Identifying mitigation strategies.
  • Using data labelling tools (for example, Amazon Mechanical Turk).

2.2 Performing feature engineering.

• Identifying and extracting features from data sets, including from data sources such as text, speech, image, public datasets, etc. (AWS Documentation: Feature Processing, Feature engineering, Amazon Textract, Amazon Textract features)
• Analyzing and evaluating feature engineering concepts (binning, tokenization, outliers, synthetic features, one-hot encoding, reducing dimensionality of data) (AWS Documentation: Data Transformations Reference, Building a serverless tokenization solution to mask sensitive data, ML-powered anomaly detection for outliers, ONE_HOT_ENCODING, Running Principal Component Analysis, Perform a large-scale principal component analysis)

2.3 Analyzing and visualizing data for ML.

• Creating Graphs (scatter plot, time series, histogram, box plot) (AWS Documentation: Using scatter plots, Run a query that produces a time series visualization, Using histograms, Using box plots)
• Interpreting descriptive statistics (correlation, summary statistics, p-value)
• Performing cluster analysis (for example, hierarchical, diagnosis, elbow plot, cluster size).

3. Understand Modeling (36%)

3.1 Framing business problems as ML problems.

• Determining when to use and when not to use ML (AWS Documentation: When to Use Machine Learning)
• Knowing the difference between supervised and unsupervised learning
• Selecting from among classification, regression, forecasting, clustering, recommendation, and foundation models. (AWS Documentation: K-means clustering with Amazon SageMaker, Building a customized recommender system in Amazon SageMaker)

3.2 Selecting the appropriate model(s) for a given ML problem.

• XGBoost, logistic regression, k-means, linear regression, decision trees, random forests, RNN, CNN, ensemble, transfer learning, and large language models (LLMs) (AWS Documentation: XGBoost Algorithm, K-means clustering with Amazon SageMaker, Forecasting financial time series, Amazon Forecast can now use Convolutional Neural Networks, Detecting hidden but non-trivial problems in transfer learning models)
• Expressing intuition behind models

3.3 Training ML models.

• Splitting data between training and validation (for example, cross validation). (AWS Documentation: Train a Model, Incremental Training, Managed Spot Training, Validate a Machine Learning Model, Cross-Validation, Model support, metrics, and validation, Splitting Your Data)
• Understanding optimization techniques for ML training (for example, gradient descent, loss functions, convergence).
• Choosing appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
  • Choosing appropriate compute platforms (Spark or non-Spark).
• Updating and retraining Models (AWS Documentation:Retraining Models on New Data, Automating model retraining and deployment)
  • Batch vs. real-time/online

3.4 Performing hyperparameter optimization.

• Performing Regularization (AWS Documentation:Training Parameters)
  • Drop out
  • L1/L2
• Performing Cross validation (AWS Documentation: Cross-Validation)
• Model initialization
• Understanding neural network architecture (layers and nodes), learning rate, and activation functions.
• Understanding tree-based models (number of trees, number of levels).
• Understanding linear models (learning rate)

3.5 Evaluating ML models.

• Avoiding overfitting and underfitting
  • Detecting and handling bias and variance (AWS Documentation: Underfitting vs. Overfitting, Amazon SageMaker Clarify Detects Bias and Increases the Transparency, Amazon SageMaker Clarify)
• Evaluating metrics (for example, area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
• Interpreting confusion matrix (AWS Documentation: Custom classifier metrics)
• Offline and online model evaluation (A/B testing) (AWS Documentation: Validate a Machine Learning Model, Machine Learning Lens)
• Comparing models using metrics (time to train a model, quality of model, engineering costs) (AWS Documentation: Easily monitor and visualize metrics while training models, Model Quality Metrics, Monitor model quality)
• Cross validation (AWS Documentation: Cross-Validation, Model support, metrics, and validation)

4. Learn About Machine Learning Implementation and Operations (20%)

4.1 Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

• Log and monitor AWS environments (AWS Documentation:Logging and Monitoring)
  • AWS CloudTrail and AWS CloudWatch (AWS Documentation: Logging Amazon ML API Calls with AWS CloudTrail, Log Amazon SageMaker API Calls, Monitoring Amazon ML, Monitor Amazon SageMaker)
  • Build error monitoring solutions (AWS Documentation: ML Platform Monitoring)
• Deploying to multiple AWS Regions and multiple Availability Zones. (AWS Documentation: Regions and Endpoints, Best practices)
• AMI and golden image (AWS Documentation: AWS Deep Learning AMI)
• Docker containers (AWS Documentation: Why use Docker containers for machine learning development, Using Docker containers with SageMaker)
• Deploying Auto Scaling groups (AWS Documentation: Automatically Scale Amazon SageMaker Models, Configuring autoscaling inference endpoints)
• Rightsizing resources, for example:
  • Instances (AWS Documentation: Ensure efficient compute resources on Amazon SageMaker)
  • Provisioned IOPS (AWS Documentation: Optimizing I/O for GPU performance tuning of deep learning)
  • Volumes (AWS Documentation: Customize your notebook volume size, up to 16 TB)
• Performing Load balancing (AWS Documentation: Managing your machine learning lifecycle)
• Following AWS best practices (AWS Documentation: Machine learning best practices in financial services)

4.2 Recommending and implementing the appropriate ML services and features for a given problem.

• ML on AWS (application services)
  • Amazon Poly (AWS Documentation: Amazon Polly, Build a unique Brand Voice with Amazon Polly)
  • Amazon Lex (AWS Documentation: Amazon Lex, Build more effective conversations on Amazon Lex)
  • Amazon Transcribe (AWS Documentation: Amazon Transcribe, Transcribe speech to text in real time)
  • Amazon Q
• Understanding AWS service quotas (AWS Documentation: Amazon SageMaker endpoints and quotas, Amazon Machine Learning endpoints and quotas, System Limits)
• Determining when to build custom models and when to use Amazon SageMaker built-in algorithms.
• Understanding AWS infrastructure (for example, instance types) and cost considerations.
  • Using spot instances to train deep learning models using AWS Batch (AWS Documentation: Train Deep Learning Models on GPUs)

4.3 Applying basic AWS security practices to ML solutions.

• AWS Identity and Access Management (IAM) (AWS Documentation: Controlling Access to Amazon ML Resources, Identity and Access Management in AWS Deep Learning Containers)
• S3 bucket policies (AWS Documentation: Using Amazon S3 with Amazon ML, Granting Amazon ML Permissions to Read Your Data from Amazon S3)
• Security groups (AWS Documentation: Secure multi-account model deployment with Amazon SageMaker, Use an AWS Deep Learning AMI)
• VPCs (AWS Documentation: Securing Amazon SageMaker Studio connectivity, Direct access to Amazon SageMaker notebooks, Building secure machine learning environments)
• Encryption and anonymization (AWS Documentation: Protect Data at Rest Using Encryption, Protecting Data in Transit with Encryption, Anonymize and manage data in your data lake)

4.4 Deploying and operationalizing ML solutions.

• Exposing endpoints and interacting with them (AWS Documentation: Creating a machine learning-powered REST API, Call an Amazon SageMaker model endpoint)
• Understanding ML models.
• Performing A/B testing (AWS Documentation: A/B Testing ML models in production, Dynamic A/B testing for machine learning models)
• Retrain pipelines (AWS Documentation: Automating model retraining and deployment, Machine Learning Lens)
• Debug and troubleshoot ML models (AWS Documentation:Debug Your Machine Learning Models, Analyzing open-source ML pipeline models in real time, Troubleshoot Amazon SageMaker model deployments)
  • Detecting and mitigating drop in performance (AWS Documentation: Identify bottlenecks, improve resource utilization, and reduce ML training costs, Optimizing I/O for GPU performance tuning of deep learning training)
  • Monitoring performance of the model (AWS Documentation: Monitor models for data and model quality, bias, and explainability, Monitoring in-production ML models at large scale)

AWS Certified Machine Learning Specialty Exam FAQs

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AWS Certification Exam Policy

Amazon Web Services (AWS) has established a comprehensive set of certification policies to ensure a secure, standardized, and equitable testing environment for all candidates. These policies are designed to uphold the integrity of the AWS Certification Program and cover essential areas such as exam retakes, score reporting, and the use of unscored questions for research and evaluation purposes.

– Exam Retake Policy

Candidates who do not pass an AWS certification exam are required to wait a minimum of 14 days before attempting the exam again. While there is no restriction on the number of retakes, each attempt is subject to the full exam fee. This waiting period and retake structure are in place to encourage thorough preparation and maintain the value and credibility of AWS certifications.

– Scoring and Results

The AWS Certified Machine Learning – Specialty (MLS-C01) exam results are reported as either pass or fail. Scoring is based on a scaled score system, ranging from 100 to 1,000, with a minimum passing score of 750. This scoring method helps normalize results across different versions of the exam, which may vary slightly in difficulty.

Candidate performance is evaluated against a standard set by AWS experts, in alignment with industry best practices. The score report may also include a performance breakdown by domain, providing insight into strengths and areas for improvement. It is important to note that the exam employs a compensatory scoring model, meaning candidates are not required to pass each individual section. Instead, passing is determined by the overall score, allowing strengths in one area to offset weaknesses in another.

AWS Certified Machine Learning Specialty Exam Study Guide

Step 1: Understand the Exam Blueprint and Objectives

Begin your preparation by reviewing the official AWS MLS-C01 exam guide, available on the AWS Certification website. This document outlines the domains covered in the exam, such as data engineering, exploratory data analysis, modeling, machine learning implementation, and operationalization. Each domain is assigned a weight, which helps you identify high-priority areas. By understanding what topics are tested and how much they contribute to your overall score, you can create a structured study plan that aligns with the exam’s expectations.

Step 2: Leverage Official AWS Training Resources

AWS offers a range of free and paid training courses specifically tailored to help candidates prepare for this certification. Start with foundational courses like “Machine Learning Essentials for Business and Technical Decision Makers” and gradually move to advanced offerings such as “The Machine Learning Pipeline on AWS”. These courses are available through the AWS Training and Certification portal and provide both theoretical knowledge and practical use cases relevant to the exam.

Step 3: Use AWS Skill Builder for Structured Learning Paths

Explore the AWS Skill Builder platform, which offers curated learning plans specifically designed for the MLS-C01 exam. These learning paths provide a guided experience, taking you through progressively complex ML concepts while incorporating quizzes and labs. Identify and complete modules where you feel less confident to close any knowledge gaps and reinforce critical concepts.

Step 4: Gain Practical Experience Through AWS Builder Labs

Hands-on practice is essential. Use AWS Builder Labs to work on real-time machine learning tasks in a sandbox environment. These labs allow you to simulate practical scenarios such as data preprocessing, training models with Amazon SageMaker, deploying models, and setting up pipelines. This practical exposure will deepen your understanding of how to apply AWS tools and services in a machine learning context.

Step 5: Explore AWS Cloud Quest and AWS Jam

To reinforce your skills in an engaging and interactive manner, consider using AWS Cloud Quest: Machine Learning. This gamified learning experience challenges you with real-world ML scenarios within a virtual environment. Additionally, participating in AWS Jam events gives you access to scenario-based challenges that simulate real-world issues machine learning engineers face in the cloud. These activities are excellent for developing problem-solving abilities and applying theoretical concepts in practical settings.

Step 6: Join Study Groups and Community Forums

Connect with others preparing for the MLS-C01 exam by joining online study groups, forums, or local meetups. Platforms such as Reddit, LinkedIn groups, and Discord communities often host active discussions, resource sharing, and peer support. Engaging with a community can provide motivation, new perspectives, and answers to complex questions you may encounter during your preparation.

Step 7: Take Official and Third-Party Practice Exams

Simulate the real test environment by taking timed practice exams. These assessments help you evaluate your readiness, identify weak areas, and improve time management skills. Start with the official AWS practice test, then move on to reliable third-party mock exams that mirror the format and difficulty level of the actual exam. After each test, thoroughly review your answers and focus on understanding the rationale behind each correct option.

Step 8: Review, Refine, and Reinforce

As your exam date approaches, revisit key concepts, review your notes, and focus on high-weighted exam domains. Use flashcards or summary sheets for quick revision. Prioritize understanding over memorization and continue practicing hands-on tasks to reinforce your skills. Once you feel confident, schedule your exam via the AWS Certification Portal with the option of taking it at a Pearson VUE center or online. Ensure your testing environment is quiet, stable, and free from distractions if you choose the online proctored format.