Certification Overview
To earn the Machine Learning Operations (MLOps) Engineer Associate certification, candidates must pass one required exam.
📌 Important
- Microsoft lists this as a single-exam certification path.
- There are no mandatory prerequisite exams or certifications required for this certification.
- However, Microsoft recommends experience with machine learning, Azure Machine Learning, DevOps pipelines, and model deployment workflows.
Track Details & Exam Requirements
🔹 Associate Exam (Required)
AI-401: Designing and Implementing a Microsoft Azure AI Solution
This exam measures the ability to implement machine learning operations practices, including model training, deployment, monitoring, and lifecycle management.
Exam Details
- Exam Code: AI-401
- Exam Name: Designing and Implementing a Microsoft Azure AI Solution
- Exam Duration: ~120 minutes
- Passing Score: 700 / 1000
- Exam Type: Microsoft role-based certification exam
- Delivery: Pearson VUE (online proctoring or test center)
- Languages: Multiple languages supported by Microsoft
- Certification Level: Associate
What This Certification Validates
With the MLOps Engineer Associate certification, you demonstrate the ability to:
- Implement machine learning pipelines and workflows
- Deploy machine learning models into production environments
- Monitor and maintain model performance
- Automate ML lifecycle processes using DevOps practices
- Implement versioning, testing, and CI/CD for machine learning systems
- Manage model governance and lifecycle management
These skills help organizations deploy scalable and reliable AI systems using MLOps best practices.
Exam Skills Measured
The AI-401 exam evaluates several major MLOps domains.
1. Design and Implement ML Pipelines (25–30%)
Candidates must understand how to:
- Build automated machine learning pipelines
- Implement data preparation and feature engineering workflows
- Configure pipeline orchestration and scheduling
2. Manage Model Training and Experimentation (20–25%)
Candidates should be able to:
- Configure model training environments
- Manage experiments and model versioning
- Track model performance metrics
3. Deploy and Manage Machine Learning Models (25–30%)
This domain focuses on:
- Deploying models to cloud environments
- Managing model endpoints and APIs
- Scaling and maintaining model deployments
4. Monitor and Maintain ML Solutions (15–20%)
Candidates must understand how to:
- Monitor model performance and drift
- Implement alerting and logging systems
- Maintain model lifecycle and retraining strategies
Validity & Recertification
Microsoft role-based certifications expire annually.
To renew certification:
- Complete a free renewal assessment on Microsoft Learn before the certification expiration date.
Recommended Experience
Although there are no formal prerequisites, Microsoft recommends candidates have:
- Experience with machine learning workflows and pipelines
- Knowledge of Azure Machine Learning services
- Familiarity with DevOps and CI/CD pipelines
- Experience with Python, data science tools, and model deployment processes
This certification is considered an associate-level AI and machine learning certification.
Who Should Pursue This Certification?
This certification is ideal for professionals such as:
- Machine Learning Engineers
- MLOps Engineers
- AI Platform Engineers
- Data Scientists implementing production ML systems
- Cloud AI engineers managing ML deployments
Career Benefits
- Demonstrates expertise in machine learning operations and automation
- Validates skills in Azure AI and ML deployment workflows
- Supports careers in AI engineering and MLOps platforms
- Recognized Microsoft certification for machine learning operations professionals
- Shows employers you can deploy and maintain production-grade AI systems
Summary
The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate certification validates a candidate’s ability to design and implement machine learning operations workflows using Azure.
Key points
- Requires one exam: AI-401
- No prerequisite certification required
- Focuses on MLOps pipelines, model deployment, monitoring, and lifecycle management
- Associate-level Microsoft AI certification.
