AI-300: Machine Learning Operations Engineer Associate

About this course

About the Exam: AI-300 – Operationalizing Machine Learning and Generative AI Solutions

The AI-300 exam measures your ability to deploy, manage, monitor, and optimize machine learning and generative AI solutions in production environments. This exam focuses on key areas such as MLOps workflows, GenAIOps infrastructure, model lifecycle management, and performance optimization in real-world scenarios.

The certification covers how professionals operationalize AI systems using Azure services, including deploying models, managing endpoints, and maintaining performance at scale. Candidates are expected to understand how machine learning pipelines, generative AI applications, and cloud infrastructure work together to support reliable AI solutions.

The exam includes topics related to implementing machine learning operations, where candidates deploy models to real-time and batch endpoints, monitor performance, and apply safe rollout and rollback strategies. This involves working with model monitoring, drift detection, and automated retraining workflows.

Another major area focuses on designing and configuring GenAIOps infrastructure. Candidates should understand how to create and manage Foundry environments, configure identity and access control, implement secure networking, and deploy infrastructure using automation tools such as Bicep and Azure CLI.The exam also evaluates generative AI quality assurance and observability. Candidates are expected to configure evaluation frameworks using metrics such as relevance, groundedness, coherence, and fluency, as well as implement logging, tracing, and monitoring for troubleshooting and performance analysis.

Additional topics include optimizing generative AI systems, such as tuning retrieval-augmented

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