AWS Certified Machine Learning Engineer – Associate

MLA-C01 · ● Active · Associate · AWS

Overview: The AWS Certified Machine Learning Engineer – Associate (MLA-C01) validates the ability to design, build, operationalize, deploy, and maintain machine learning (ML) solutions end-to-end on Amazon Web Services. Launched October 2024 in beta; general availability October 2024. Replaces the retiring Machine Learning Specialty (MLS-C01) as AWS's primary ML certification. Focuses on MLOps and SageMaker automation rather than deep statistical model training.

Exam facts

Vendor source — AWS Certified Machine Learning Engineer Associate ↗

Official exam guide — AWS MLA-C01 Exam Guide PDF ↗

Exam objectives — AWS MLA-C01 Exam Objectives ↗

About

AWS Certified Machine Learning Engineer – Associate (MLA-C01) is AWS's modern ML credential for engineers who operationalize, deploy, and maintain production machine learning systems on AWS infrastructure. Launched October 2024, it replaces portions of the retiring Machine Learning Specialty (MLS-C01, retiring March 31, 2026). Unlike MLS-C01, which emphasized statistical model building from scratch, MLA-C01 focuses on SageMaker-managed workflows, CI/CD for ML pipelines, model monitoring, and MLOps practices. It targets practitioners who build end-to-end ML solutions using AWS managed services rather than custom model development.

Domain context — Data / AI

The Data / AI domain encompasses data engineering, analytics, machine learning, and emerging AI engineering roles. MLA-C01 sits at the ML Engineer (Associate) level — the role that owns the full ML lifecycle: data preparation, model development, deployment, and monitoring.

Read full deep dive — Data / AI / ML Domain →

Topics covered

The MLA-C01 exam blueprint is divided into four domains, each with published weights:

• Data Preparation for Machine Learning (28%) — Data pipeline design, AWS Glue, Amazon Athena, data validation, feature engineering, AWS Glue DataBrew
• ML Model Development (26%) — Algorithm selection, hyperparameter tuning, model training at scale, evaluation metrics, SageMaker Training, SageMaker Autopilot, SageMaker Canvas
• Deployment and Orchestration of ML Workflows (22%) — Model registry, inference endpoints (real-time and batch), multi-model endpoints, A/B testing, CI/CD for ML (AWS Step Functions, AWS CodePipeline, EventBridge), SageMaker Pipelines
• ML Solution Monitoring, Maintenance, and Security (24%) — Model drift detection, SageMaker Model Monitor, CloudWatch metrics, cost optimization, IAM for ML workloads, data encryption (KMS), model bias detection (SageMaker Clarify), explainability

Source: AWS MLA-C01 Exam Guide ↗

Common skills at Data / AI · Associate

Shared content for the Data / AI domain at Associate level — not specific to this cert.

• ML lifecycle fundamentals — End-to-end model creation: problem definition, data, training, evaluation, deployment, monitoring
• Supervised and unsupervised learning — Regression, classification, clustering, dimensionality reduction
• Feature engineering — Feature selection, scaling, one-hot encoding, handling missing data, domain-specific feature creation
• Model evaluation metrics — Accuracy, precision, recall, F1, AUC-ROC, RMSE, confusion matrices, cross-validation
• Hyperparameter tuning — Grid search, random search, Bayesian optimization, early stopping
• Model deployment patterns — Batch inference, real-time endpoints, A/B testing, canary deployments
• MLOps fundamentals — Model versioning, experiment tracking, automated retraining pipelines, monitoring and alerting

Recommended courses at Data / AI · Associate

Course-selection rule: Each course listed is specifically for MLA-C01. Generic "AWS ML" courses or SageMaker overviews are not included. If a provider offers no MLA-C01-specific course, that provider is omitted.

Practice exams

Note: MeasureUp does not yet offer MLA-C01 (as of May 2026). Tutorials Dojo and Whizlabs are the primary third-party practice exam providers for this cert.

Books

Book rule: All titles are either MLA-C01-specific study guides or foundational ML/MLOps references. Older MLS-C01 study guides are NOT recommended as the exam blueprint differs significantly.

Typical job titles at Data / AI · Associate

Machine Learning Engineer · MLOps Engineer · AI Engineer · Cloud ML Engineer · ML Solutions Engineer · Data Scientist (application-focused)

(Job titles drawn from current job-board postings that list MLA-C01 or equivalent AWS ML cert as required or preferred. Roles vary by company stage and region.)

Salary

Salary rule: Figures reflect roles requiring AWS MLA-C01 or equivalent ML certification at the Associate level (1–3 years post-cert). Salaries vary by company size (FAANG > startup), geography (SF Bay Area +20–40% vs. national average), and specialization (GenAI specialists +10–20% premium). Remote-capable roles enable geographic arbitrage in high-cost markets.

Skills validated

Cert-specific — what MLA-C01 actually tests, distinct from the shared "Common skills" above.

• Amazon SageMaker Studio — Integrated ML development environment; notebook kernels; project setup
• SageMaker Training Jobs — Built-in algorithms (LinearLearner, XGBoost, Image Classification, Object Detection); Bring Your Own Container (BYOC); distributed training; managed spot training
• SageMaker Autopilot — Automated ML; feature engineering; algorithm selection; model tuning; explainability (SHAP values)
• SageMaker Canvas — No-code ML for business users; forecasting, classification, regression
• SageMaker Feature Store — Feature management; feature groups; real-time and batch feature retrieval
• SageMaker Model Registry — Model versioning; lineage tracking; approval workflows
• SageMaker Inference — Real-time inference endpoints (single-model, multi-model); batch transform jobs; A/B testing; auto-scaling
• SageMaker Pipelines — ML workflow orchestration; step definitions; conditional execution; integration with Step Functions
• SageMaker Model Monitor — Data drift detection; model quality drift; feature importance baseline; bias metrics
• SageMaker Clarify — Model explainability (SHAP, LIME); bias detection; feature attribution
• AWS Glue — ETL service; job types (PySpark, Scala, Python shell); crawlers; data catalog
• AWS Glue DataBrew — Data profiling; recipe-based data cleaning; visual data preparation
• Amazon Athena — SQL queries on S3; integration with Glue Data Catalog; partitioning strategies
• AWS Step Functions — Workflow orchestration; state machines; error handling; integration with Lambda, SageMaker, SNS
• AWS CodePipeline — CI/CD for ML; source (CodeCommit, GitHub), build (CodeBuild), deploy stages
• AWS EventBridge — Event-driven ML pipelines; triggers for automated retraining
• Amazon S3 — Data lake patterns; bucket policies; versioning; lifecycle rules for ML data retention
• AWS Key Management Service (KMS) — Encryption at rest for ML artifacts, models, data
• AWS Identity and Access Management (IAM) — ML-specific roles; least-privilege policies for SageMaker, Glue, S3
• Amazon CloudWatch — Metrics for SageMaker jobs; custom metrics; dashboards; alarms for model performance
• AWS Lambda — Serverless inference; preprocessing; post-processing steps in ML pipelines
• Amazon Bedrock — Foundation model access (Claude, Llama, Stable Diffusion); prompt engineering; fine-tuning managed models
• ML model evaluation — Evaluation metrics by task type (RMSE, MAE for regression; precision, recall, F1, AUC-ROC for classification); confusion matrices; validation strategies
• Hyperparameter tuning — SageMaker Automatic Model Tuning; tuning job definition; objective metric selection; early stopping
• Batch vs. real-time inference patterns — Trade-offs; cost optimization; latency requirements

Related certifications

• Stacks with: AWS Certified AI Practitioner (AIF-C01) ↗ — Broader AI/ML foundations; complementary breadth
• Prerequisite for: AWS Certified Generative AI Developer – Professional (emerging 2025–2026) — Advanced LLM and RAG patterns on SageMaker
• Replaces in spirit: AWS Certified Machine Learning Specialty (MLS-C01) ↗ — Retiring March 31, 2026. MLS-C01 emphasized statistical model building; MLA-C01 emphasizes operationalization and MLOps.
• Stacks with (data pipeline): AWS Certified Data Engineer – Associate (DEA-C01) ↗ — Data preparation is 28% of MLA-C01; DEA-C01 covers ETL/ELT in depth
• Equivalents at this level: Google Cloud Professional ML Engineer ↗ · Azure AI-102 (retiring) / AI-103 (emerging) ↗ · Databricks Certified ML Engineer Associate ↗ — (Files not yet created; note explicitly)
• Vendor overview: AWS Ecosystem Deep Dive ↗

Sources

• AWS Certified Machine Learning Engineer Associate (Official Page)
• AWS MLA-C01 Exam Guide PDF
• AWS Machine Learning Specialty (MLS-C01) Retirement Notice (March 31, 2026)
• AWS Skill Builder — MLA-C01 Exam Prep
• Tutorials Dojo — MLA-C01 Practice Exams
• Whizlabs — MLA-C01 Practice Tests
• Glassdoor — Machine Learning Engineer Salary
• Levels.fyi — ML Engineer Salaries
• Robert Half 2026 Tech Salary Guide
• AWS Ecosystem Deep Dive — D04
• Data / AI / ML Domain Deep Dive — DOM08

Last verified: 2026-05-01
Parent ecosystem: AWS Ecosystem Deep Dive
Parent domain: Data / AI / ML Domain
Vendor overview: AWS Certification Ecosystem