---
title: AWS Certified Machine Learning Engineer — Associate
description: The hands-on AWS ML certification covering data preparation, model development, deployment, monitoring, and MLOps — replacing the retiring MLS-C01 Specialty for most practitioners.
url: https://www.factualminds.com/certifications/aws-machine-learning-engineer-associate/
examCode: MLA-C01
level: associate
publishDate: 2026-05-15
updateDate: 2026-05-15
---

# AWS Certified Machine Learning Engineer — Associate

> MLA-C01 is the practical, engineering-oriented ML certification AWS launched in 2024 — focused on building, deploying, and operating ML systems on SageMaker, Bedrock, and adjacent AWS services.

## Why MLA-C01 exists

AWS replaced the Machine Learning Specialty (MLS-C01) with the Machine Learning Engineer Associate (MLA-C01) to align with how ML actually ships in 2024–2026. The new exam emphasizes:

- **MLOps over algorithms** — Pipelines, monitoring, model registry, drift detection are heavier than algorithm internals.
- **GenAI alongside predictive ML** — Bedrock, fine-tuning, RAG patterns, prompt engineering are explicit objectives.
- **Cost-aware deployment** — Multi-model endpoints, serverless inference, Bedrock provisioned throughput show up in exam questions.
- **Security and governance** — Model risk, bias, drift, data classification at production scale.

## Recommended 5-week study plan

**Week 1 — Data preparation (10 hours)**
SageMaker Data Wrangler labs, Glue Data Quality, Feature Store walkthrough. Read the SageMaker Feature Store deep-dive.

**Week 2 — Model development (10 hours)**
Train a built-in XGBoost model and a custom-script PyTorch model. Run SageMaker AMT. Try JumpStart fine-tuning on a small dataset.

**Week 3 — Deployment patterns (10 hours)**
Deploy real-time, serverless, async, and multi-model endpoints. Try Inference Recommender. Set up SageMaker Pipelines with EventBridge triggers.

**Week 4 — Monitoring + GenAI (10 hours)**
SageMaker Model Monitor walkthrough. Bedrock Knowledge Bases + Guardrails labs. Build a small RAG application end-to-end.

**Week 5 — Practice exams + weak areas (8 hours)**
Two Tutorial Dojo practice sets. Review every wrong answer. Re-read the AWS _Well-Architected Machine Learning Lens_.

## Related FactualMinds Content

- [AWS SageMaker ML Consulting](/services/aws-sagemaker/)
- [Generative AI on AWS](/services/generative-ai-on-aws/)
- [Amazon Bedrock Consulting](/services/aws-bedrock/)

## Exam Details

- **Duration:** 130 minutes
- **Questions:** 65
- **Passing score:** 720 / 1000
- **Cost:** $150 USD
- **Format:** Multiple choice, multiple response, ordering, matching, and case study questions
- **Validity:** 3 years
- **Recommended experience:** 1 year of ML engineering or data engineering experience, plus hands-on AWS exposure

## Exam Domains

### Data Preparation for Machine Learning (28%)

- Data ingestion: S3, Glue, EMR Serverless, Kinesis Data Streams / Firehose, MSK
- Storage decisions for ML data: S3, S3 Tables (Iceberg), Aurora, DynamoDB
- Feature engineering: SageMaker Feature Store, AWS Glue DataBrew, pandas on SageMaker Studio
- Data quality: SageMaker Data Wrangler, Glue Data Quality, deequ
- Bias detection on training data: SageMaker Clarify, Macie for sensitive-data scanning

### ML Model Development (26%)

- SageMaker built-in algorithms: XGBoost, Linear Learner, BlazingText, DeepAR
- Bring-your-own-container and bring-your-own-script training modes
- SageMaker JumpStart for pre-trained model deployment and fine-tuning
- Hyperparameter tuning with SageMaker AMT (automatic model tuning)
- Distributed training: data parallelism, model parallelism, SageMaker Smart Sifting
- Amazon Bedrock model customization: continued pre-training, fine-tuning Nova / Claude where supported

### Deployment and Orchestration of ML Workflows (22%)

- SageMaker endpoint types: real-time, serverless, asynchronous, batch transform, multi-model
- Inference optimization: SageMaker Inference Recommender, SageMaker Neo, instance selection
- SageMaker Pipelines for orchestration; EventBridge triggers; Step Functions for cross-service flows
- Model registry, model lineage, model approval workflows
- A/B testing and traffic shifting between model versions on SageMaker endpoints
- Bedrock provisioned throughput for predictable cost on high-volume inference

### ML Solution Monitoring, Maintenance, and Security (24%)

- SageMaker Model Monitor: data drift, model quality, feature attribution drift, bias drift
- CloudWatch metrics and Application Signals for ML services
- Retraining triggers and automated retraining workflows
- Security: IAM roles for SageMaker, VPC endpoints, KMS encryption for training data and artifacts
- Cost optimization: Spot training, Savings Plans for SageMaker, multi-model endpoints
- Bedrock Guardrails for generative use cases, content-filter monitoring

---

*Source: https://www.factualminds.com/certifications/aws-machine-learning-engineer-associate/*