AWS Machine Learning Certification (MLA-C01) Guide 2026: Cost, Domains + Best Prep
Last updated: May 2026. Written by Josh Hutcheson. See our review methodology.
By Josh Hutcheson · E-Learning Specialist
Reviewing online learning platforms since 2019.
The 60-second guide: AWS now has TWO ML-related certs: the legacy AWS Certified Machine Learning – Specialty (MLS-C01) — valid through retirement — and the new AWS Certified Machine Learning Engineer – Associate (MLA-C01) launched 2024. For 2026 candidates, take MLA-C01: it’s Associate-level (more accessible), focuses on production ML engineering, and aligns better with current job market expectations. Cost $150 USD, 130 minutes, 65 questions. Best prep: Microsoft Learn does NOT have AWS content; instead use the Udacity AWS Machine Learning Engineer Nanodegree (94 hrs of hands-on SageMaker + Lambda + Step Functions content).
By the numbers (AWS Certified Machine Learning Engineer Associate (MLA-C01)):
- AWS Machine Learning Engineer Associate launched 2024 — newest AWS associate cert. Source: aws.amazon.com.
- AWS ML Engineers earn $145K-$200K base mid-level in US tech hubs (Levels.fyi, Apr 2026). Source: www.levels.fyi.
- AWS Specialty (MLS-C01) is being phased out in favor of MLA-C01 — Associate-level is more accessible and aligned with current job market. Source: aws.amazon.com.
Risk reversal: AWS retake allowed after 14 days. Each attempt $150. Most candidates pass second attempt after focusing on weak domains.
MLS-C01 vs MLA-C01: which exam should you take in 2026?
| Exam | Status | Take it? |
|---|---|---|
| MLS-C01 (Specialty) | Active but legacy — expected to retire 2026-2027 | Only if you already started prep. Otherwise pivot to MLA-C01. |
| MLA-C01 (Associate, ML Engineer) | Current (launched 2024) | Yes — this is THE current AWS ML cert. |
What is MLA-C01?
The AWS Certified Machine Learning Engineer – Associate validates your ability to build production ML systems on AWS — not just train models. Heavy emphasis on:
- SageMaker for end-to-end ML workflows.
- Pipeline orchestration with AWS Step Functions and Lambda.
- Model deployment, monitoring (SageMaker Model Monitor, CloudWatch), and lifecycle management.
- Data engineering for ML (S3, Glue, Feature Store).
- Production concerns: security (IAM for ML), cost, scaling, A/B testing.
Should you pursue MLA-C01?
Take it if:
- You’re an ML engineer (or aspiring one) targeting AWS-shop employers.
- You have ~1+ year of ML experience and 6+ months on AWS.
- You want a cert that matches modern MLOps job postings (SageMaker + production deployment).
Skip if:
- You’re a data scientist (model-building only) without engineering interests — this is a deployment-heavy cert.
- You target Azure or GCP — get the equivalent (Azure has DP-100 + AI-102; GCP has Professional Machine Learning Engineer).
Exam format
| Detail | MLA-C01 |
|---|---|
| Cost | $150 USD |
| Duration | 130 minutes |
| Questions | 65 |
| Passing score | 720 / 1000 |
| Validity | 3 years |
| Recommended experience | 1+ year ML + 6+ months AWS |
The 4 exam domains
Domain 1: Data preparation for ML (28%)
Data ingestion (S3, Kinesis), AWS Glue, Feature Store, data quality, sampling strategies, transformations.
Domain 2: ML model development (26%)
SageMaker built-in algorithms, training jobs, hyperparameter tuning, AutoML (SageMaker Autopilot), framework integration (PyTorch, TensorFlow, Scikit-learn).
Domain 3: Deployment and orchestration of ML workflows (22%)
SageMaker endpoints (real-time, async, serverless, batch transform), multi-model endpoints, Lambda + Step Functions integration, CI/CD for ML.
Domain 4: ML solution monitoring, maintenance, and security (24%)
SageMaker Model Monitor, CloudWatch metrics, A/B testing patterns, IAM for ML resources, encryption at rest + in transit.
Salary impact
- Junior ML Engineer (AWS): $115K-$145K base in US tech hubs.
- Mid-level ML Engineer (3-5 yrs + MLA-C01): $145K-$190K base.
- Senior ML Engineer / Staff ML: $200K-$280K base + equity at FAANG.
MLA-C01 specifically signals production-ML readiness, which is increasingly the bottleneck in ML hiring.
Best ways to prepare for MLA-C01
MLA-C01 is new (launched 2024) so the prep ecosystem is still maturing. Honest ranking:
#1 (genuinely best for hands-on + cert): Udacity AWS Machine Learning Engineer Nanodegree
Unusual for me to put Udacity at #1, but for MLA-C01 it’s earned. Udacity AWS ML Engineer Nanodegree (nd189) — intermediate level, 94 hrs — covers SageMaker end-to-end, AWS Lambda for ML, Step Functions ML pipelines. These ARE the central topics on MLA-C01.
Why this works as #1: MLA-C01 is heavily focused on building production ML systems, and Udacity’s program drops you into actually building them. Most other prep options are theoretical.
$399/month + ~$50-120/month AWS costs. ~5-6 months at 10 hrs/week. Read our full review.
#2 (free, official): AWS Skill Builder ML Engineer learning path
Official AWS path. Heavy on SageMaker tutorials. Free. Best free option if you can’t justify the Nanodegree cost.
#3 (alternative, newer course): Stephane Maarek MLA-C01 on Udemy
Stephane Maarek’s MLA-C01 course on Udemy ($15-30). Newer course, still maturing, but Maarek’s reputation makes it a credible exam-prep option.
#4 (essential): Practice tests
Tutorials Dojo (Jon Bonso) MLA-C01 ($14.99) — recommended. AWS Official Practice Exam ($20). Whizlabs MLA-C01 alternative.
#5 (legacy alternative, only if already in progress): MLS-C01 path
The legacy AWS Specialty (MLS-C01) is being phased out but accepts existing prep materials including Linux Academy / A Cloud Guru’s older course. Only relevant if you started MLS-C01 prep before MLA-C01 launched.
10-week study plan
- Weeks 1-3: SageMaker fundamentals + data prep (Domain 1 + 2).
- Weeks 4-5: Domain 3 (deployment, Step Functions, Lambda for ML).
- Weeks 6-7: Domain 4 (monitoring, security, lifecycle).
- Weeks 8-9: Practice tests + weak spot review.
- Week 10: Final review + exam.
Frequently Asked Questions
Should I take MLS-C01 or MLA-C01?
MLA-C01. The Specialty (MLS-C01) is being phased out. MLA-C01 is more aligned with current ML engineering job postings.
How does MLA-C01 compare to Azure DP-100?
DP-100 covers Azure Machine Learning Studio + SDK. MLA-C01 covers AWS SageMaker. Pick based on your target stack. AWS vs Azure comparison.
Is MLA-C01 worth it?
Yes for ML engineers in AWS-shop environments. Adds ~10-15% to median offer.
What background do I need?
Python, basic ML concepts (supervised vs unsupervised, train/test splits, common metrics), and 6+ months AWS exposure.
What jobs can I get?
ML Engineer, MLOps Engineer, Senior ML Engineer, Applied Scientist roles. Median offer with cert: $145K-$200K base in US tech hubs.
Related guides
Cert path: AWS Data Engineer (DEA-C01) · AWS Developer (DVA-C02) · AWS Cloud Practitioner
Best courses: Udacity AWS ML Engineer Nanodegree · Udacity Data Scientist Nanodegree