AI-300 is Microsoft’s current associate-level exam for the Microsoft Certified: Machine Learning Operations Engineer Associate credential. The exam focuses on operationalizing both traditional machine learning and generative AI on Azure, bringing MLOps and GenAIOps together under one AI operations role. The current Microsoft study guide was last updated March 5, 2026, and the certification page lists the exam as an intermediate Azure Machine Learning and Microsoft Foundry credential.
The current AI-300 blueprint has five weighted areas: design and implement an MLOps infrastructure at 15–20%; implement machine learning model lifecycle and operations at 25–30%; design and implement a GenAIOps infrastructure at 20–25%; implement generative AI quality assurance and observability at 10–15%; and optimize generative AI systems and model performance at 10–15%. Microsoft lists 120 minutes, a USD 165 U.S. price with regional variation, and a passing score of 700.
The role combines traditional ML operations with generative AI operations
Microsoft expects candidates to work with both Azure Machine Learning and Microsoft Foundry. Traditional ML responsibilities include training, optimizing, registering, deploying, monitoring, and retraining models. GenAIOps responsibilities include deploying foundation models, managing prompts, evaluating agents or applications, monitoring quality and cost, and improving retrieval or fine-tuning performance.
This means AI-300 is not simply a renamed data-science exam. It is an operations and lifecycle exam centered on repeatable deployment, automation, governance, observability, and optimization.
MLOps infrastructure starts with workspaces, assets, compute, and identity
The first domain covers Machine Learning workspaces, datastores, compute targets, identity and access management, data assets, environments, components, and registries. Candidates should understand how a workspace becomes a governed engineering environment rather than only a place to run notebooks.
Infrastructure design also includes secure network access and the ability to share reusable assets across workspaces through registries.
Infrastructure as code and GitHub Actions are explicit exam objectives
Microsoft expects candidates to deploy Machine Learning workspaces and related resources using Bicep and Azure CLI, automate provisioning with GitHub Actions, integrate source control securely, and manage machine-learning projects with Git.
A GitHub Actions foundation is useful because AI-300 treats infrastructure and model lifecycle as automation problems. Production ML should be recreatable from reviewed source rather than from undocumented portal changes.
Model lifecycle operations form the largest weighted domain
The 25–30% model lifecycle area includes MLflow tracking, automated ML, notebooks for experimentation, hyperparameter tuning, training scripts, distributed training, training pipelines, and model comparison. Candidates need to connect experimentation with a production path rather than stopping when a model produces a good metric.
A general machine-learning model workflow can reinforce training concepts, but AI-300 expects Azure-specific operationalization around those concepts.
Registration, versioning, and responsible evaluation link development to production
The current guide includes packaging a feature retrieval specification with a model artifact, registering MLflow models, evaluating models using responsible-AI principles, and managing lifecycle actions such as archiving. Versioning creates the evidence trail that connects a deployed endpoint to the training and evaluation state that produced it.
Model governance is therefore part of deployment readiness, not a separate administrative task.
Production deployment includes endpoint choice, testing, rollout, and rollback
Candidates should know how to deploy models as real-time or batch endpoints using managed inference, test and troubleshoot endpoints, and implement progressive rollout and safe rollback strategies. The correct endpoint model follows latency, throughput, traffic, cost, and operational requirements.
A deployment is not complete until the team knows how to validate it and return to a known-good version if production metrics deteriorate.
GenAIOps infrastructure centers on Microsoft Foundry
The 20–25% GenAIOps infrastructure domain includes Foundry resources and projects, managed identities, RBAC, private networking, Bicep, Azure CLI, foundation-model deployment, model selection, versioning, production strategies, and provisioned throughput units for high-volume workloads.
The same infrastructure disciplines used in MLOps—identity, network, version control, and automation—carry into generative AI, but the assets and runtime characteristics differ.
Prompt management is treated as versioned production work
The live blueprint explicitly includes prompt design, prompt variants, comparative performance, and Git-based prompt version control. That is a useful signal about how Microsoft frames GenAIOps: prompts are production artifacts whose changes can alter behavior and therefore need history, testing, and controlled release.
Prompt experimentation should create evidence about quality rather than relying on subjective preference.
Generative AI quality assurance uses measurable evaluation criteria
The 10–15% quality-and-observability domain includes test datasets, data mapping, groundedness, relevance, coherence, fluency, harmful-content or safety evaluation, and automated evaluation workflows using built-in or custom metrics. Candidates should understand why GenAI quality needs multiple dimensions rather than one “accuracy” number.
The exam also covers continuous monitoring, latency, throughput, response time, token and resource cost, logging, tracing, and debugging.
RAG and fine-tuning complete the optimization domain
The final 10–15% includes retrieval thresholds, chunk size, retrieval strategies, embedding-model selection, hybrid semantic/keyword search, RAG evaluation, A/B testing, advanced fine-tuning, synthetic data, fine-tuned-model monitoring, and production lifecycle management.
The audience profile also signals the expected prerequisite depth. Microsoft says candidates should have a data-science background, Python experience, and an entry-level understanding of DevOps practices, including GitHub Actions and command-line tools. That combination explains why the exam does not teach model training from first principles or DevOps from scratch. It tests the integration point where data-science assets become governed production systems.
Registries are important in the MLOps infrastructure domain because reusable assets often need to move across workspaces or teams. A registry can support controlled sharing of components, environments, or models without requiring each project to rebuild them. Candidates should connect registries to reuse, governance, and version consistency rather than memorizing them as another workspace feature.
Compute-target design should also be treated as an operational decision. Interactive experimentation, training, batch inference, and production serving can have different needs for scale, startup, isolation, and cost. The exam is likely to reward candidates who choose compute according to workload behavior and lifecycle stage rather than one default cluster pattern.
MLflow appears because experiment tracking and model lifecycle need a common evidence trail. Parameters, metrics, artifacts, model versions, and registration status help teams reproduce why one model was promoted and another was rejected. That traceability becomes especially important during rollback or post-incident analysis.
Responsible-AI evaluation belongs before production because a technically accurate model can still create unacceptable behavior for particular groups or use cases. The current guide explicitly calls out responsible-AI principles during model evaluation, reinforcing that model governance is part of engineering quality rather than a later compliance add-on.
Progressive rollout is another strong operational clue. A new model can be sent to a limited portion of production traffic, compared against a known-good version, and rolled back if quality or service metrics degrade. This pattern connects deployment, observability, and release governance in one objective.
Provisioned throughput units in Foundry are included because high-volume GenAI workloads can require predictable serving capacity. Candidates should understand the difference between a flexible serverless consumption model and provisioned capacity chosen for consistent throughput. The decision depends on traffic shape, latency expectations, cost, and capacity guarantees.
GenAI observability also extends beyond model output. Token consumption, resource use, response time, tracing, and logs can reveal cost or orchestration problems even when generated text appears acceptable. Operations teams need a way to distinguish a quality regression from an infrastructure or budget problem.
Hybrid search appears in the RAG optimization domain because keyword and semantic retrieval have different strengths. Combining them can improve recall across exact terms, domain jargon, and concept similarity. The exam expects candidates to reason about retrieval strategy as an engineering choice, not only as a model prompt problem.
Fine-tuning with synthetic data adds another governance boundary. Synthetic examples can increase coverage or reduce dependence on sensitive source data, but quality, representativeness, and leakage still need evaluation. The current blueprint therefore treats fine-tuning as a full lifecycle activity from data generation to production monitoring.
AI-300 also expects candidates to understand collaboration across roles. Data scientists may own experimentation, DevOps engineers may own release automation, platform teams may own network and identity, and stakeholders may own business thresholds. The operations engineer has to connect those responsibilities so a model or agent can move safely from experimentation into production.
Source control is therefore more than code history. Infrastructure definitions, prompts, pipelines, deployment configuration, and model metadata all need enough traceability that a production state can be reconstructed. Microsoft’s inclusion of Git and GitHub Actions across the blueprint reinforces that production AI is an engineering lifecycle rather than a sequence of portal clicks.
The exam’s weighting also suggests where depth matters most. The two largest areas are model lifecycle operations and GenAIOps infrastructure, which together represent 45–55% of the scored skills. Candidates should still cover every domain, but they should be especially fluent in how assets move from development to production and how production systems are monitored and improved.
Another useful readiness test is to distinguish quality, safety, performance, and cost. A GenAI system can be relevant but unsafe, safe but slow, fast but expensive, or cheap but poorly grounded. AI-300 expects candidates to treat these dimensions separately enough that the corrective action matches the actual problem.
The strongest preparation therefore combines traditional ML discipline with the newer operational concerns of foundation models and agents. If you can explain identity, deployment, versioning, evaluation, observability, rollback, and optimization for both model types, you are studying the role Microsoft currently describes rather than an older data-science certification.
Within the broader Microsoft certification ecosystem, AI-300 is the operational AI credential. The current exam rewards candidates who can move from infrastructure to model or agent deployment, measure real production behavior, and improve the system without losing governance or repeatability.