If you're a data scientist looking to add a cloud cert to your resume, you've probably narrowed it down to two options: Microsoft's DP-100 (Azure Data Scientist Associate) or AWS's MLA-C01 (Machine Learning Engineer Associate).
Both are solid. Both are recognized. Both will make recruiters' eyes light up.
But they're fundamentally different exams testing fundamentally different skill sets. And choosing wrong could waste months of study time.
I've passed both. Let me save you the research.
The TL;DR
| DP-100 | MLA-C01 | |
|---|---|---|
| Focus | Azure ML workspace, AutoML, pipelines | AWS ML services, MLOps, deployment |
| Difficulty | Medium | Medium-Hard |
| Best for | Data scientists who deploy on Azure | ML engineers who build on AWS |
| Exam fee | $165 | $150 |
| Questions | ~55 | 65 |
| Time | 100 min | 170 min |
| Passing | 700/1000 | 720/1000 |
What the DP-100 Actually Tests
Microsoft wants to know if you can use Azure Machine Learning studio to run the full ML lifecycle. That means:
Designing and preparing an ML solution (20-25%) — Choosing between Azure ML compute instances vs compute clusters vs serverless. Understanding when to use AutoML vs custom training. Data asset creation and datastores.
Exploring data and training models (25-35%) — This is the meat. Scikit-learn, PyTorch, and TensorFlow on Azure. But here's the key — the exam cares more about HOW you set up training jobs on Azure than your ML theory knowledge. Know your mlflow logging, experiment tracking, and environment configurations.
Preparing a model for deployment (20-25%) — Model registration, managed endpoints (online vs batch), blue-green deployments. The deployment questions are increasingly common and increasingly specific.
Monitoring models (10-15%) — Data drift detection, model performance monitoring, responsible AI dashboards. Small section but easy points.
The DP-100 is Azure-centric. If you know scikit-learn and can navigate Azure ML studio, you're 70% of the way there.
What the MLA-C01 Actually Tests
AWS takes a different approach. The MLA-C01 is less about one specific service and more about knowing which of AWS's 47 ML-related services to use when.
Data Engineering (20%) — S3, Glue, Kinesis for ML data pipelines. Overlaps heavily with the DEA-C01.
Exploratory Data Analysis (24%) — SageMaker notebooks, feature engineering, data visualization. AWS expects you to know pandas/numpy basics but won't test theory deeply.
Modeling (36%) — The biggest domain. SageMaker training jobs, hyperparameter tuning, built-in algorithms (XGBoost, Linear Learner, etc.), custom containers. You need to know when to use each built-in algorithm and why.
ML Implementation and Operations (20%) — SageMaker endpoints, A/B testing, model monitoring, CI/CD for ML. MLOps is increasingly the focus.
The MLA-C01 requires broader AWS knowledge. If you're not comfortable with IAM, S3, VPCs, and basic networking, you'll struggle with the infrastructure questions.
Which One Gets You Hired?
I pulled job postings from LinkedIn in February 2026. Here's what I found:
- "Azure Data Scientist" or "DP-100": ~3,200 job postings mentioning these terms
- "AWS ML Engineer" or "MLA-C01": ~2,800 job postings
Azure slightly edges out AWS in raw numbers, but the AWS jobs tend to pay 5-10% more on average.
If you work at a Microsoft shop → DP-100. No question.
If you work at an AWS shop → MLA-C01. Note: the old ML Specialty is retiring March 31, 2026, so the MLA-C01 is now your only option.
If you're freelancing or consulting → Get both. Seriously.
How I Studied for Each
DP-100 study (6 weeks):
- Microsoft Learn paths (free, comprehensive)
- Azure ML studio hands-on labs
- DP-100 practice questions on ExamCert — the scenario-based questions were extremely close to the real exam
MLA-C01 study (8 weeks):
- AWS Skill Builder (free tier)
- SageMaker hands-on workshops
- AWS MLA-C01 practice exam on ExamCert — especially helpful for the service selection questions
Both available for $4.99 lifetime access on ExamCert — which is kind of insane when you consider Whizlabs charges $30+ per exam. Money-back guarantee if you don't pass, so zero risk.
The Secret Nobody Tells You
Here's the real secret about both exams: they test cloud platform knowledge more than ML knowledge. If you're a decent data scientist, you already know the ML part. What you need to learn is the PLATFORM part.
I spent 80% of my study time learning Azure ML studio / SageMaker interfaces and only 20% reviewing ML concepts. That ratio worked perfectly.
My Recommendation
If you can only get one: match your current or target employer's cloud platform.
If you want maximum career flexibility: start with whichever platform your current job uses, then get the other one within 6 months.
Pick one. Start today. Both paths lead somewhere good.
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