Microsoft Certified: Operationalizing Machine Learning and Generative AI Solutions

AI-300 · ● Beta · Associate · Microsoft

Exam status note: The AI-300 exam entered public beta in March 2026 and is scheduled for general availability in May 2026. It targets machine learning operations engineers and data scientists who design, implement, and manage production-grade ML and GenAI solutions on Azure.

Name correction: This is not a "Cloud AI Architect Expert" certification — it is an Associate-level operational MLOps / GenAIOps certification.

Exam facts

Vendor source — Microsoft Certified: Operationalizing Machine Learning and Generative AI Solutions ↗

Official exam guide — Study Guide for Exam AI-300 ↗

Exam objectives — Exam AI-300 Skills Measured ↗

About

The AI-300 is Microsoft's newest Associate-level certification for machine learning operations (MLOps) engineers, data scientists, and DevOps professionals who build, deploy, and operate production ML and generative AI systems on Azure. Launched in beta March 2026, it validates hands-on expertise in Azure Machine Learning, model lifecycle management, CI/CD automation, monitoring, and responsible AI practices. It replaces the retiring Azure Data Scientist Associate (DP-100) on June 1, 2026, and signals Microsoft's shift from "data science" to "ML operations" as the core professional path for data engineers and ML practitioners entering production systems.

Domain context — Data & AI

Public cloud machine learning operations, MLOps infrastructure, generative AI deployment, model monitoring, Azure ML services, and responsible AI governance. Cross-listed with Cloud and DevOps/Automation due to heavy infrastructure, automation, and observability components.

Read full deep dive — Microsoft Azure + M365 → | Data & AI Domain → | DevOps/Automation Domain →

Topics covered

• Design and implement an MLOps infrastructure (15–20% of exam)

  • Set up Azure Machine Learning workspaces, compute targets, and environments
  • Configure Git integration, source control, and repository management
  • Implement infrastructure as code (IaC) with Bicep and Azure Resource Manager templates
  • Deploy ML infrastructure using CI/CD pipelines (GitHub Actions, Azure Pipelines)

• Implement machine learning model lifecycle and operations (25–30%)

  • Train, validate, and register ML models using Azure ML SDK and AutoML
  • Implement model deployment strategies (batch, real-time, streaming endpoints)
  • Monitor model performance, detect data drift, and retrain workflows
  • Version and manage model artifacts using Azure ML Model Registry

• Design and implement a GenAIOps infrastructure (20–25%)

  • Deploy and configure generative AI models and large language models (LLMs)
  • Build agentic workflows and multi-agent orchestration using Microsoft Foundry
  • Implement prompt engineering, RAG (retrieval-augmented generation) pipelines
  • Configure Azure OpenAI endpoints and manage model access and consumption

• Implement generative AI quality assurance and observability (10–15%)

  • Evaluate LLM output quality using metrics and human feedback
  • Implement logging, tracing, and observability for GenAI applications
  • Detect hallucinations, bias, and safety issues in generative AI systems
  • Monitor token usage, cost, and latency for GenAI services

• Optimize generative AI systems and model performance (10–15%)

  • Fine-tune and customize LLMs for domain-specific tasks
  • Implement prompt optimization and model compression techniques
  • Optimize inference latency, throughput, and cost
  • Scale GenAI workloads on Azure infrastructure

Source: Official exam guide ↗

Common skills at Data & AI · Associate

Shared content for the Data & AI domain at Associate level — not specific to this cert.

• Python programming (pandas, NumPy, scikit-learn, TensorFlow, PyTorch)
• SQL and relational database query optimization
• Data pipeline design, ETL/ELT workflows, and batch processing
• Cloud data services (Azure Data Factory, Databricks, Synapse Analytics)
• Statistical analysis, hypothesis testing, and experimental design
• Model evaluation metrics, cross-validation, and hyperparameter tuning
• Data governance, privacy, and ethical AI principles
• Logging, monitoring, and observability fundamentals
• Git version control and collaborative development workflows

Recommended courses

Course note: The official Microsoft Learn course (AI-300T00) is free and most current; instructor-led variants are available through training partners. Beta exam candidates gained early access to this course starting March 2026.

Practice exams

Practice note: Official Microsoft practice materials are limited during beta; community-contributed question banks are available but carry caveat of unofficial content. MeasureUp and Whizlabs typically release official practice tests within 60 days of exam general availability.

Books

Book note: The official Pearson Exam Ref for AI-300 was not yet released at time of writing (May 2026); likely available by GA. "Designing Machine Learning Systems" and "Azure Machine Learning in Action" cover core MLOps concepts directly applicable to the exam.

Typical job titles at Data & AI · Associate

MLOps Engineer · Machine Learning Operations Specialist · Data Engineer (MLOps focus) · AI Infrastructure Engineer · Cloud ML Engineer · Azure ML Specialist

(Job titles drawn from current job-board postings that list MLOps, machine learning operations, or Azure ML infrastructure as core responsibilities.)

Salary

Salary note: AI-300 is new (beta May 2026) so no certification-specific salary data exists yet. Ranges above reflect typical MLOps and machine learning engineer compensation. MLOps specialists with production deployment experience command 15–30K premium over pure model-building roles.

Skills validated

• Azure Machine Learning service (workspace setup, compute, environments, datastores)
• ML pipeline orchestration (parameterized training, batch scoring, automated retraining)
• Model deployment and inference (batch endpoints, real-time REST/gRPC, streaming endpoints)
• Model monitoring and drift detection (performance baselines, data/prediction drift alerts)
• CI/CD for ML (GitHub Actions, Azure Pipelines, integration with ML training)
• Infrastructure as Code (Bicep, ARM templates, Terraform for ML infrastructure)
• Generative AI operationalization (Azure OpenAI, prompt engineering, RAG pipelines, agentic workflows)
• Responsible AI (bias detection, fairness metrics, explainability, safety guardrails)
• Python and Azure ML SDK (model training, experiment tracking, logging)
• Observability and logging (Application Insights, Azure Log Analytics, custom metrics)

Related certifications

• Stacks with: Microsoft Certified: Azure Data Engineer Associate (DP-203) ↗ — shares data pipeline infrastructure knowledge
• Stacks with: Microsoft Certified: Azure Solutions Architect Expert (AZ-305) ↗ — for architects overseeing ML infrastructure
• Prerequisite for: Microsoft Certified: Agentic AI Business Solutions Architect (AB-100) ↗ — higher-level AI architecture certification
• Replaces: Azure Data Scientist Associate (DP-100) ↗ — retires June 1, 2026
• Equivalent at this level: AWS Certified Machine Learning Engineer – Associate (MLE-C01) ↗
• Vendor overview: Microsoft Vendor Overview ↗

Sources

• Microsoft Certified: Operationalizing Machine Learning and Generative AI Solutions ↗
• Study Guide for Exam AI-300 ↗
• Course AI-300T00: Operationalize machine learning and generative AI solutions ↗
• New Certification for machine learning operations (MLOps) engineers ↗
• New Certification for architects of agentic AI business solutions ↗
• AI learning hub – Start your AI learning journey ↗
• Microsoft AI & ML Engineering Professional Certificate (Coursera) ↗
• Levels.fyi: Microsoft Machine Learning Engineer Salary ↗
• Glassdoor: Microsoft AI Engineer Salary ↗
• Azure Machine Learning in Action (Manning) ↗
• Designing Machine Learning Systems (O'Reilly) ↗

Last verified: 2026-05-01

Parent ecosystem: Microsoft Azure + M365 →

Parent domain: Data & AI →

Vendor overview: Microsoft Vendor Overview →