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MLS-C01
MLS-C01 Amazon Web Services

Machine Learning Specialty

The AWS Certified Machine Learning – Specialty (MLS‑C01) training teaches practitioners to design, build, and operate end‑to‑end ML solutions on AWS, covering data engineering, exploratory analysis, modeling, and deployment.

180
Minutes
65
Questions
750/1000
Passing Score
$300
Exam Cost
4
Languages

Who Should Take This

Data engineers, data scientists, and ML developers with at least two years of hands‑on experience in building machine‑learning pipelines on AWS are ideal candidates. They seek to validate their expertise, deepen their knowledge of AWS ML services, and demonstrate the ability to deliver production‑grade models at scale.

What's Covered

1 Create data repositories for ML, identify and implement data ingestion and transformation solutions using AWS data services.
2 Sanitize and prepare data for modeling, perform feature engineering, and analyze and visualize data distributions and relationships.
3 Frame business problems as ML problems, select appropriate models and algorithms, train and evaluate ML models, and perform hyperparameter optimization.
4 Build ML solutions for performance, availability, and scalability, implement ML model deployment and inference pipelines, and monitor production models.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Response

Scoring Method

Scaled scoring from 100 to 1000, minimum passing score of 750

Delivery Method

Pearson VUE testing center or online proctored

Recertification

Recertify every 3 years by passing the current exam or earning a higher-level AWS certification.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

70 learning goals
1 Domain 1: Data Engineering
3 topics

Data storage and ingestion for ML

  • Implement S3 data lake architectures for ML workloads using appropriate storage classes, partitioning schemes, and lifecycle policies to optimize cost and access patterns for training data.
  • Implement real-time data ingestion pipelines using Kinesis Data Streams and Kinesis Data Firehose with appropriate shard counts, buffer intervals, and delivery configurations for streaming ML feature inputs.
  • Analyze ingestion architecture tradeoffs across batch, micro-batch, and streaming patterns and select the appropriate ingestion strategy based on latency requirements, data volume, and downstream model consumption needs.

Data transformation and ETL for ML

  • Implement AWS Glue ETL jobs with crawlers, data catalogs, and PySpark or Python shell scripts to transform raw data into ML-ready datasets with schema evolution handling.
  • Implement EMR cluster configurations with appropriate instance types, scaling policies, and Spark job parameters to process large-scale data transformations for ML feature pipelines.
  • Implement SageMaker Data Wrangler and SageMaker Processing jobs to perform data transformations, joins, and feature computation at scale within the SageMaker ecosystem.
  • Analyze ETL service tradeoffs among Glue, EMR, SageMaker Processing, and Athena and select the appropriate transformation service based on data size, complexity, cost, and integration requirements.

Data pipeline orchestration and governance

  • Implement data pipeline orchestration using Step Functions, Glue workflows, and SageMaker Pipelines to automate end-to-end data preparation with error handling and retry logic.
  • Implement data governance controls using Lake Formation, Glue Data Catalog, and IAM policies to enforce access permissions, data lineage tracking, and column-level security for ML datasets.
  • Implement Athena queries against Glue Data Catalog tables to perform ad-hoc analysis, data validation, and quality checks on partitioned datasets stored in S3.
  • Design end-to-end data engineering strategies that balance cost, data freshness, feature reuse, governance compliance, and operational maintainability for ML initiatives across batch and streaming paths.
2 Domain 2: Exploratory Data Analysis
4 topics

Data sanitization and preparation

  • Implement data cleaning techniques including missing value imputation strategies, outlier detection and handling, deduplication, and type casting to produce consistent, well-formed datasets for model training.
  • Implement data labeling workflows using SageMaker Ground Truth with built-in task types, custom templates, annotation consolidation, and active learning to produce high-quality training labels.
  • Analyze data quality issues including class imbalance, label noise, selection bias, and distribution skew and determine appropriate remediation strategies such as resampling, SMOTE, and stratified splitting.

Feature engineering

  • Implement numerical feature transformations including normalization, standardization, log transforms, binning, and polynomial feature generation to improve model convergence and predictive power.
  • Implement categorical feature encoding using one-hot encoding, label encoding, target encoding, and embedding-based approaches and select the appropriate method based on cardinality and model type.
  • Implement text feature engineering using bag-of-words, TF-IDF, n-grams, word embeddings, and tokenization strategies appropriate for NLP model inputs.
  • Implement SageMaker Feature Store to create, populate, and serve features from online and offline stores with point-in-time correctness for training and inference consistency.
  • Analyze feature transformation pipelines for information leakage risk, target leakage, and bias amplification and design train-test split strategies that preserve temporal ordering and prevent data contamination.

Statistical analysis and visualization

  • Apply descriptive statistics and correlation analysis to characterize dataset distributions, identify multicollinearity, and detect anomalous patterns that affect model training.
  • Apply visualization techniques including scatter plots, histograms, box plots, heatmaps, and dimensionality reduction plots (PCA, t-SNE) to discover feature relationships and data structure.
  • Implement QuickSight dashboards and SageMaker notebook-based visualizations to communicate data exploration findings and support feature selection decisions for modeling teams.

Dimensionality reduction and feature selection

  • Implement dimensionality reduction techniques including PCA, t-SNE, and feature importance ranking to reduce feature space while preserving predictive signal for model training.
  • Analyze feature selection strategies and determine when to apply filter methods, wrapper methods, or embedded methods based on dataset size, feature count, and model interpretability requirements.
  • Interpret exploratory analysis findings to prioritize feature hypotheses, recommend data collection improvements, and determine modeling experimentation direction across supervised and unsupervised approaches.
3 Domain 3: Modeling
7 topics

Problem framing and ML approach selection

  • Translate business requirements into well-defined ML problem types (classification, regression, clustering, recommendation, forecasting, anomaly detection) with appropriate success metrics and constraints.
  • Analyze when to use supervised, unsupervised, semi-supervised, or reinforcement learning approaches and determine when a rule-based or heuristic solution is more appropriate than a trained ML model.
  • Formulate modeling strategies that align business impact, data availability, risk tolerance, explainability expectations, and time-to-deployment constraints across cross-functional teams.

SageMaker built-in algorithms

  • Apply SageMaker built-in supervised algorithms (Linear Learner, XGBoost, KNN, Factorization Machines) with correct data formats, hyperparameters, and training channel configurations for tabular prediction tasks.
  • Apply SageMaker built-in unsupervised algorithms (K-Means, PCA, Random Cut Forest, IP Insights) with correct input modes and hyperparameters for clustering, dimensionality reduction, and anomaly detection.
  • Apply SageMaker built-in NLP and sequence algorithms (BlazingText, Seq2Seq, Object2Vec, LDA, NTM) with appropriate data preparation and hyperparameter settings for text and sequence modeling tasks.
  • Apply SageMaker built-in image algorithms (Image Classification, Object Detection, Semantic Segmentation) with proper data formats (RecordIO, augmented manifest), transfer learning, and multi-GPU training configurations.
  • Analyze SageMaker algorithm selection tradeoffs and determine the optimal built-in algorithm given data modality, training data volume, latency requirements, and interpretability constraints.

Custom and deep learning models on SageMaker

  • Implement custom training containers using SageMaker script mode with TensorFlow, PyTorch, or Scikit-learn frameworks including entry point scripts, dependency management, and training channel configuration.
  • Implement deep learning architectures (CNNs, RNNs, LSTMs, Transformers) on SageMaker with distributed training using data parallelism, model parallelism, and appropriate instance families (P3, P4, G4).
  • Implement transfer learning and fine-tuning workflows using SageMaker JumpStart pretrained models and custom model adaptation for domain-specific tasks with limited training data.
  • Analyze deep learning architecture choices and determine when to use CNNs, RNNs, attention mechanisms, or pretrained models based on data modality, sequence length, compute budget, and accuracy requirements.

Hyperparameter tuning and training optimization

  • Implement SageMaker Automatic Model Tuning (hyperparameter optimization) with Bayesian, random, and hyperband strategies using appropriate objective metrics, parameter ranges, and early stopping configurations.
  • Apply regularization techniques (L1, L2, dropout, early stopping, data augmentation) to control overfitting and implement cross-validation strategies to estimate generalization performance.
  • Analyze training outcomes to diagnose overfitting, underfitting, convergence failures, and optimization instability using learning curves, loss plots, and validation metric trends across iterative experiments.

Model evaluation and validation

  • Apply classification evaluation metrics (accuracy, precision, recall, F1, AUC-ROC, AUC-PR, confusion matrix) and select the appropriate metric based on class distribution and business cost of errors.
  • Apply regression evaluation metrics (MSE, RMSE, MAE, R-squared, MAPE) and time-series evaluation metrics (WAPE, MASE) with appropriate holdout and backtesting validation schemes.
  • Implement SageMaker Clarify to detect bias in training data and model predictions using pre-training and post-training bias metrics and generate model explainability reports with SHAP values.
  • Analyze model evaluation results to determine production readiness by interpreting calibration curves, threshold tuning tradeoffs, and statistical significance of performance differences between candidate models.
  • Design evaluation strategies that account for fairness, robustness, dataset shift, and regulatory compliance requirements before production rollout across multiple model versions.

AWS AI services for common ML tasks

  • Apply Amazon Comprehend for NLP tasks including entity recognition, sentiment analysis, key phrase extraction, language detection, topic modeling, and custom classification with training data.
  • Apply Amazon Rekognition for computer vision tasks including image classification, object and face detection, content moderation, text-in-image detection, and custom label training.
  • Apply Amazon Textract for document analysis including form extraction, table extraction, and expense processing with confidence scores and human review workflows.
  • Apply Amazon Translate, Polly, Transcribe, and Lex for language translation, speech synthesis, speech-to-text transcription, and conversational AI with custom vocabulary and language model adaptations.
  • Apply Amazon Forecast and Amazon Personalize for time-series forecasting and real-time recommendation workloads with appropriate dataset preparation, recipe selection, and campaign configuration.
  • Apply Amazon Kendra for intelligent search with data source connectors, document enrichment, and relevance tuning to provide ML-powered enterprise search.
  • Analyze AWS AI service capabilities and determine when to use a managed AI service versus training a custom SageMaker model based on customization needs, accuracy requirements, latency targets, and total cost of ownership.

SageMaker Autopilot and automated ML

  • Implement SageMaker Autopilot for automated model development including data exploration, candidate generation, and model selection with appropriate problem type configuration and objective metrics.
  • Analyze Autopilot-generated notebooks and candidate pipelines to understand feature engineering choices, algorithm selections, and hyperparameter configurations and determine when manual intervention improves results.
4 Domain 4: Machine Learning Implementation and Operations
4 topics

Model deployment and inference

  • Implement SageMaker real-time endpoints with appropriate instance types, auto-scaling policies, and multi-model or multi-container configurations to serve inference traffic at target latency.
  • Implement SageMaker batch transform jobs for offline inference on large datasets with appropriate instance counts, data splitting strategies, and output assembly configurations.
  • Implement SageMaker serverless inference and asynchronous inference endpoints for variable-traffic and long-running inference workloads with appropriate concurrency and timeout configurations.
  • Implement model optimization for inference using SageMaker Neo compilation, model quantization, and distillation techniques to reduce inference latency and deployment cost across target hardware.
  • Analyze deployment pattern tradeoffs across real-time, serverless, asynchronous, and batch inference and select the optimal pattern based on latency targets, traffic patterns, cost constraints, and payload size.

ML pipeline automation and CI/CD

  • Implement SageMaker Pipelines to automate end-to-end ML workflows with processing, training, evaluation, condition, and model registration steps including parameterization and caching.
  • Implement SageMaker Model Registry to catalog model versions with approval workflows, metadata tracking, and deployment stage transitions for governed model lifecycle management.
  • Implement blue-green and canary deployment strategies for SageMaker endpoints using production variants, traffic shifting, and CloudWatch alarm-based automatic rollback to minimize deployment risk.
  • Design MLOps strategies that integrate model versioning, automated retraining triggers, approval gates, and reproducible pipelines to establish continuous delivery for ML models across environments.

Monitoring, drift detection, and operational health

  • Implement SageMaker Model Monitor to detect data quality drift, model quality degradation, bias drift, and feature attribution drift with baseline constraints and scheduled monitoring jobs.
  • Implement CloudWatch metrics, alarms, and dashboards to monitor endpoint invocation latency, error rates, CPU/GPU utilization, and model-specific performance indicators for production ML systems.
  • Analyze monitoring signals to distinguish between data drift, concept drift, and infrastructure degradation and determine appropriate remediation including retraining, data pipeline correction, or scaling adjustments.

Security, compliance, and cost optimization for ML

  • Implement security controls for ML workloads including VPC configurations for SageMaker, IAM execution roles, KMS encryption for data at rest and in transit, and network isolation for training and inference.
  • Implement cost optimization strategies for ML workloads including Spot training instances, right-sizing endpoint instances, SageMaker Savings Plans, and managed warm pool reuse to reduce total training and inference cost.
  • Define maintainability strategies that include retraining triggers, governance checkpoints, auditability requirements, model retirement procedures, and compliance documentation for regulated ML systems.
  • Architect operational patterns that optimize reliability, cost, and compliance while supporting retraining cadence, model lifecycle governance, and multi-account ML platform management.

Hands-On Labs

20 labs ~510 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$168,000
Average Salary

Related Job Roles

Machine Learning Engineer Data Scientist ML Architect AI/ML Solutions Architect Applied Scientist

Industry Recognition

The AWS Machine Learning Specialty has been one of the highest-paying AWS certifications, validating deep ML expertise on the AWS platform. While being retired in favor of the ML Engineer Associate, existing holders demonstrate specialty-level competency in the rapidly growing AI/ML job market.

Scope

Included Topics

  • All domains and task statements in the AWS Certified Machine Learning - Specialty (MLS-C01) exam guide: Domain 1 Data Engineering (20%), Domain 2 Exploratory Data Analysis (24%), Domain 3 Modeling (36%), and Domain 4 Machine Learning Implementation and Operations (20%).
  • Specialty-level machine learning workflows on AWS spanning data ingestion, storage, transformation, feature engineering, model development, hyperparameter tuning, model evaluation, deployment, inference optimization, and operational monitoring.
  • Scenario-based architectural and operational decisions that require selecting and integrating AWS services to deliver reliable, scalable, and maintainable machine learning solutions.
  • Key AWS ML services: SageMaker (Studio, Training, Hosting, Pipelines, Feature Store, Model Monitor, Clarify, Canvas, JumpStart, Ground Truth, Autopilot, Neo, Data Wrangler, Processing), Comprehend, Rekognition, Textract, Translate, Polly, Transcribe, Forecast, Personalize, Lex, Kendra, Bedrock, S3, Glue, Kinesis (Data Streams, Data Firehose, Data Analytics), EMR, Athena, Lake Formation, Step Functions, Lambda, CloudWatch, ECR, and IAM.

Not Covered

  • General cloud architecture content not directly tied to machine learning lifecycle responsibilities assessed by MLS-C01.
  • Deep theoretical proofs and research-level derivations in statistics or optimization that are not required for applied AWS machine learning decisions.
  • Vendor-specific services outside AWS that are not needed to satisfy MLS-C01 task statements.
  • Short-lived pricing figures, promotional offers, and other unstable commercial details that are not durable domain knowledge.
  • Hands-on CLI command syntax and SDK version-specific API signatures not assessed by the exam.

Official Exam Page

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