AWS Machine Learning Engineer Associate (MLA-C01)
Build, train, and deploy production ML models on AWS
Exam Quick Facts
Exam Code
MLA-C01
Questions
65
Duration
170 minutes
Passing Score
720/1000 (~72%)
Cost
$150 USD
Valid For
3 years
Last Updated
2024
Difficulty
Advanced
AWS Certified Machine Learning Engineer – Associate Cheat Sheet (MLA-C01)
Every in-scope SageMaker feature, MLOps workflow, monitoring and security decision the MLA-C01 exam actually tests — organised by the four exam domains with runbooks and decision tables
Certification Overview
The AWS Certified Machine Learning Engineer Associate (MLA-C01) is AWS's hands-on ML certification, launched in 2024 to replace the retired ML Specialty exam. It validates your ability to build data pipelines, train models, deploy endpoints, and implement MLOps practices using AWS services.
Unlike the AI Practitioner (which is conceptual), the MLA-C01 requires practical engineering skills. You need to understand how to prepare data with Glue and SageMaker Processing, train models with SageMaker training jobs, deploy models to endpoints, and monitor them for drift and performance degradation in production.
This certification bridges the gap between data science and production engineering. It's not about developing novel algorithms — it's about taking models from notebook to production reliably, securely, and at scale using AWS infrastructure.
Who Should Take This Exam?
ML engineers, data engineers, and software engineers building ML systems on AWS should pursue this certification. If you use SageMaker, build data pipelines with Glue, or deploy models to production endpoints, this cert validates your practical skills.
It's also a strong choice for data scientists who want to demonstrate they can productionize their models, not just build them in notebooks. The industry increasingly distinguishes between "ML research" and "ML engineering," and this cert firmly establishes your engineering credentials.
Exam Domains
Data Preparation for ML
The largest domain. Covers data ingestion, transformation, and feature engineering using S3, Glue, SageMaker Processing, and SageMaker Feature Store. Includes data quality validation, handling imbalanced datasets, and building reproducible data pipelines.
ML Model Development
Tests your ability to select algorithms, train models with SageMaker training jobs, perform hyperparameter tuning, and evaluate model performance. Includes SageMaker built-in algorithms, custom training containers, and distributed training.
Deployment and Orchestration of ML Workflows
Covers deploying models to SageMaker endpoints (real-time, batch, serverless), building ML pipelines with SageMaker Pipelines and Step Functions, and implementing A/B testing for model variants.
ML Solution Monitoring, Maintenance, and Security
Tests monitoring for model drift with SageMaker Model Monitor, implementing retraining triggers, IAM roles for SageMaker, VPC configuration for training jobs, and encryption of data at rest and in transit.
Ready to test your MLA-C01 knowledge?
Practice with our free Machine Learning Engineer Associate (MLA-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice ExamStudy Strategy
Hands-on SageMaker experience is non-negotiable for this exam. Build an end-to-end ML pipeline: ingest data from S3, transform it with Glue or SageMaker Processing, train a model using a built-in algorithm, deploy it to an endpoint, and set up Model Monitor to track data drift.
Spend extra time on data preparation (28%) — this is the largest domain and covers the practical, often unglamorous work of cleaning, transforming, and validating data. Know the difference between SageMaker Processing jobs, Glue jobs, and when to use each.
The exam also tests MLOps concepts heavily. Understand SageMaker Pipelines for orchestrating training workflows, model registries for versioning, and how to implement automated retraining when drift is detected. Budget 6-8 weeks of study with active ML engineering experience.
Key AWS Services to Know
- Amazon SageMaker — training, endpoints, pipelines, Feature Store, Model Monitor
- AWS Glue — ETL jobs, crawlers, Data Catalog
- Amazon S3 — data lake storage for ML datasets
- AWS Step Functions — ML workflow orchestration
- Amazon ECR — container registry for custom training images
- AWS Lambda — lightweight inference and pipeline triggers
- Amazon CloudWatch — monitoring training jobs and endpoints
- AWS IAM — SageMaker execution roles and resource policies
- Amazon Athena — ad-hoc querying of data in S3
- Amazon Kinesis — real-time data ingestion for streaming ML
Career Impact
ML engineering is one of the fastest-growing and highest-paying specializations in tech. The MLA-C01 positions you in the intersection of software engineering and machine learning — a space where demand far exceeds supply. ML engineer roles on AWS typically pay $140K-$200K, with senior roles at top companies exceeding $250K.
This certification is particularly valuable as organizations move from ML experimentation to production deployment. Companies that have data science teams building models in notebooks are increasingly hiring ML engineers to productionize those models — and this certification proves you have the skills to do it.
Frequently Asked Questions
What's the difference between the ML Engineer and the AI Practitioner?
The AI Practitioner is conceptual — no coding required. The ML Engineer requires hands-on engineering skills: building data pipelines, training models with SageMaker, deploying endpoints, and implementing MLOps.
What AWS services should I know for the MLA-C01?
Master SageMaker (training, endpoints, pipelines, Feature Store, Model Monitor), Glue (ETL), Step Functions (orchestration), and IAM security for ML workloads.
Does the MLA-C01 replace the old ML Specialty?
Yes. AWS retired the ML Specialty (MLS-C01) and split it into the AI Practitioner (foundational) and ML Engineer Associate (hands-on). The MLA is the practical successor.
Ready to test your MLA-C01 knowledge?
Practice with our free Machine Learning Engineer Associate (MLA-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice Exam