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Microsoft Certification Guide · 2026AI-300 Certification Guide 2026: Machine Learning Operations Engineer Associate
Everything about the AI-300 exam — all five domains and weightings, why this is genuinely two disciplines in one paper, the DevOps skills a data scientist will not expect, the salary hike MLOps engineers command in India and the US, and a 12-week roadmap.
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Microsoft
AI-300 — Machine Learning Operations Engineer Associate
The credential this guide covers
Claude
Claude Certified Architect – Foundations (CCAR-F)
Paired with AI-300 in our programme — read the CCAR-F guideAI-300 (Operationalizing Machine Learning and Generative AI Solutions) earns the Microsoft Certified: Machine Learning Operations Engineer Associate credential. It is a 120-minute proctored exam, you pass at 700 out of 1000, and it covers five domains across both MLOps and GenAIOps — what Microsoft collectively calls AI operations, or AIOps. Azure Machine Learning and Microsoft Foundry are both in scope, as are Bicep, Azure CLI and GitHub Actions.
Note the naming, because it trips people up: the exam is called “Operationalizing Machine Learning and Generative AI Solutions”, but the credential you earn is “Machine Learning Operations Engineer Associate”. Search for the second one on your CV and LinkedIn — that is the title recruiters will actually recognise.
AI-300 exam at a glance
| Attribute | Detail |
|---|---|
| Exam code | AI-300 — Operationalizing Machine Learning and Generative AI Solutions |
| Certification earned | Microsoft Certified: Machine Learning Operations Engineer Associate |
| Level | Intermediate / Associate — AI Engineer role |
| Products in scope | Azure Machine Learning and Microsoft Foundry |
| Duration | 120 minutes, proctored, may include interactive components |
| Passing score | 700 out of 1000 |
| Delivery | Pearson VUE — online proctored or at a test centre |
| Languages | English only |
| Practice assessment | Available via AI Skills Navigator (sign-in required) |
| Prerequisites | None enforced — but a data science background, Python, and entry-level DevOps are assumed |
| Renewal | Expires annually; renew free via an online assessment on Microsoft Learn |
| Retake policy | 24-hour wait after a first fail; longer waits for subsequent attempts |
What is the AI-300 certification?
AI-300 certifies that you can put models into production and keep them there — both kinds of model. Microsoft’s audience profile asks for experience “training, optimizing, deploying, and maintaining traditional machine learning models by using Azure Machine Learning, in addition to experience deploying, evaluating, monitoring, and optimizing generative AI applications and agents by using Microsoft Foundry.”
That word — in addition to — is the whole exam. It is not an ML exam with some GenAI bolted on, nor a GenAI exam with legacy ML attached. It is deliberately both, and the weightings bear that out.
Microsoft asks for a data science background plus an entry-level understanding of DevOps — specifically GitHub Actions, CLIs, and infrastructure as code with Bicep and Azure CLI. Those appear directly in the skills measured. A data scientist who has never written a Bicep template or a GitHub Actions workflow has a real gap to close, and it is not a small one.
AI-300 skills measured and weightings
Five domains. Model lifecycle is the single biggest, but the more useful way to read this blueprint is by discipline rather than by domain.
AI-300 exam blueprint — share of exam by domain
Official Microsoft weightings. Bars show the midpoint of each published range.
Source: Microsoft Learn, “Study guide for Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions”. Bars use the midpoint of each published range.
Group the domains by discipline and AI-300 is almost exactly half classic MLOps and half GenAIOps. Traditional ML — model lifecycle plus MLOps infrastructure — is roughly 45% at midpoint. Generative AI — GenAIOps infrastructure, quality assurance and optimisation — is roughly 47%. A data scientist who has never touched Foundry is missing half the paper. A GenAI engineer who has never tuned hyperparameters or detected data drift is missing the other half.
The five domains as flashcards
ML model lifecycle and operations
MLflow experiment tracking, automated ML, hyperparameter tuning, distributed training for large and deep models, training pipelines and cross-job comparison. Packaging a feature retrieval specification with the model artifact, registering MLflow models, responsible-AI evaluation, archiving. Real-time and batch endpoints, progressive rollout and safe rollback. Data drift detection and retraining triggers.
25–30% · biggest domainGenAIOps infrastructure
Creating and configuring Foundry resources and project environments. Managed identities and RBAC. Private networking. Bicep templates and Azure CLI. Deploying foundation models via serverless API endpoints and managed compute, selecting models per use case, versioning, and configuring provisioned throughput units for high-volume workloads. Prompt versioning in Git with variants and comparison.
20–25% of examMLOps infrastructure
Workspaces, datastores, compute targets and workspace IAM. Data assets, environments, components, and sharing across workspaces via registries. GitHub integration with Machine Learning for secure access. Deploying workspaces with Bicep and Azure CLI. Automating provisioning with GitHub Actions. Restricting network access. Git source control for ML projects.
15–20% of examGenAI quality assurance and observability
Test datasets and data mapping for evaluation. AI quality metrics — groundedness, relevance, coherence and fluency. Risk and safety evaluations for harmful content. Automated evaluation workflows with built-in and custom metrics. Continuous monitoring in Foundry: latency, throughput, response times, token consumption and cost, plus detailed logging and tracing.
10–15% of examOptimize GenAI systems and models
RAG tuning: similarity thresholds, chunk sizes and retrieval strategies. Selecting and fine-tuning embedding models for domain accuracy. Hybrid search combining semantic and keyword retrieval. Relevance metrics and A/B testing frameworks. Advanced fine-tuning methods, synthetic data creation and management, and taking a fine-tuned model from development to production.
10–15% of examThe gap most candidates have
Two, and they are mirror images. Data scientists arrive strong on MLflow and hyperparameter tuning but have never written Bicep, wired GitHub Actions or configured RBAC. GenAI engineers arrive strong on Foundry and evaluators but have never handled data drift, distributed training or safe rollback. Diagnose which half you are before planning.
Strategy noteAI-300 vs AI-103 vs AI-200
Microsoft’s new AI track has three developer-level codes and they are genuinely distinct. This is the clearest way to tell them apart.
| Dimension | AI-300 | AI-103 | AI-200 |
|---|---|---|---|
| Credential | ML Operations Engineer Associate | AI Apps and Agents Developer Associate | AI Cloud Developer Associate |
| The job | Operate models in production | Build AI features and agents | Build the back-end infrastructure |
| Classic ML | Yes — roughly half the exam | No | No |
| Foundry | Yes — GenAIOps side | Yes — core of the exam | No |
| IaC / GitHub Actions | Examined | CI/CD for Foundry only | Not examined |
| Kubernetes / KEDA | Not examined | Not examined | Examined |
| Best for | Data scientists and ML engineers going to production | Developers shipping AI features | Backend engineers on AI platforms |
Is AI-300 worth it?
Where it genuinely helps
- MLOps is a named, budgeted role. Unlike some AI titles, “MLOps engineer” has existed long enough to have real job descriptions, salary bands and hiring managers who know what it means.
- The dual scope is the differentiator. Plenty of people can train a model, and plenty can call an LLM. Far fewer can operate both under one deployment and monitoring discipline — which is exactly what this credential evidences.
- Evaluation metrics are examined properly. Groundedness, relevance, coherence and fluency, plus safety evaluations, are named skills. Most teams still cannot describe how they measure whether their GenAI output is any good.
- A practice assessment exists. Unlike AI-200, Microsoft has published an official practice assessment for AI-300 on AI Skills Navigator.
Where it will not carry you
- It is not an entry point. Microsoft assumes a data science background and DevOps familiarity. Two prerequisites, both real, neither enforced.
- Half of it may be unfamiliar. Whichever discipline you come from, roughly half this exam is the other one. Plan for that rather than discovering it in week ten.
- English only, and it expires annually like every Microsoft associate credential.
What MLOps engineers actually earn
The honest framing first. No certificate has a salary attached to it. What the market pays for is the role — and MLOps has an advantage over newer AI titles: it is an established, budgeted function with recognised bands.
What the hike looks like in rupees
The reported specialist premium, applied at its midpoint (~33%) to published Indian engineering bands.
Entry · 0–2 yrs
Mid · 3–5 yrs
Senior · 6–9 yrs
Lead · 10+ yrs
MLOps and AI engineer pay by market
India and the US shown as two separate charts, because rupee and dollar bands are different measures on different scales — a shared axis would misrepresent both.
India — annual CTC
MLOps / AI engineer roles, ₹ lakh per annum
Bar length maps the upper bound of each band against a ₹52 L scale. Sources: Glassdoor India AI Architect/Engineer data (July 2026, avg ~₹35 L, range ~₹20.75–47.75 L); published Azure AI engineer India ranges of ₹15–30 L and ₹20–50 L.
United States — annual base
Azure AI / MLOps engineer roles, US$ thousands
Bar length maps each figure against a $160K scale. Source: published 2026 Azure AI engineer compensation data — average ~$111,000, typical range $90,000–$129,500, top earners around $145,000+.
Generalist vs MLOps engineer
Generalist software engineer vs MLOps / AIOps engineer
India, ₹ lakh per annum — modelled from the reported 25–40% specialist premium
Method: generalist bands are drawn from published Indian software and data engineering ranges; the specialist bar applies the reported 25–40% AI-specialist premium at its midpoint (~33%). Illustrative modelling, not survey data — treat it as direction, not a quotation.
Compensation figures are compiled from independent, publicly available industry sources and are shown for role context. They are not a guarantee of pay in any specific market, company or outcome, and 360DT does not promise a salary result from any certification or programme.
Why Microsoft merged MLOps and GenAIOps into one exam
1. Organisations run both, on one team
Very few companies replaced their classic ML with generative AI. They added it. The forecasting model and the RAG assistant now sit in the same organisation, often maintained by the same people, and Microsoft coined AIOps to describe exactly that combined discipline.
2. The operational problems rhyme
Data drift and RAG relevance decay are the same category of problem: a system that worked at launch and quietly stopped working. So are retraining triggers and re-evaluation workflows. Weighting the exam across both disciplines is a bet that the operational instincts transfer even when the technology does not.
3. Evaluation became the hard part
A whole domain covers groundedness, relevance, coherence, fluency and safety evaluation, plus token and cost monitoring. Deploying a generative system is easy; proving it is still behaving, and knowing before your users do, is what production actually requires.
Your 12-week AI-300 roadmap
Weighted to the five domains and structured so the discipline you are weaker in gets the earlier, longer blocks. Assume 8–10 focused hours per week.
Diagnose your gap, fix the DevOps baseline
Decide honestly whether you are the data scientist or the GenAI engineer in this exam’s audience. Then close the shared prerequisite: Git, GitHub Actions workflows, Azure CLI, and reading and writing a Bicep template. All three are examined directly.
MLOps infrastructure
Workspaces, datastores, compute targets, workspace IAM. Data assets, environments, components, and registries for sharing across workspaces. GitHub integration for secure access, Bicep and CLI deployment, GitHub Actions provisioning, network restriction. Covers the 15–20% domain.
Model lifecycle and operations
The biggest domain. MLflow tracking, AutoML, hyperparameter tuning, distributed training, training pipelines, comparing runs. Feature retrieval specs packaged with the artifact, model registration, responsible-AI evaluation, archiving. Real-time and batch endpoints, progressive rollout, safe rollback. Drift detection and retraining triggers. 25–30% of the paper.
GenAIOps infrastructure
Foundry resources and project environments. Managed identities and RBAC. Private networking. Bicep and CLI deployment. Foundation model deployment via serverless endpoints and managed compute, model selection, versioning, and provisioned throughput units. Prompt versioning in Git with variants you can compare. Covers the 20–25% domain.
Quality assurance and observability
Build test datasets with data mapping. Implement groundedness, relevance, coherence and fluency metrics. Configure risk and safety evaluations for harmful content. Automate evaluation with built-in and custom metrics. Then continuous monitoring: latency, throughput, token consumption, cost, tracing and logging.
Optimisation and fine-tuning
Tune RAG: similarity thresholds, chunk sizes, retrieval strategies. Select and fine-tune embedding models for a domain. Implement hybrid semantic-plus-keyword search. Prove improvement with relevance metrics and A/B testing. Then advanced fine-tuning, synthetic data, and moving a fine-tuned model to production.
Integrated pipeline and practice assessment
Build one pipeline that trains and registers a classic model and deploys and evaluates a generative one, both provisioned by Bicep and triggered by GitHub Actions. Then take Microsoft’s official practice assessment on AI Skills Navigator and review by domain.
Consolidate and sit the exam
Final pass over your weaker discipline, run the exam sandbox so interactive components hold no surprises, and book. Register with a personal Microsoft account — work-account records are lost if you leave the organisation.
Microsoft explicitly recommends registering with a personal MSA account. If you register with an organisational work or school account and later leave that organisation, your exam records are unrecoverable.
Learn from Microsoft Certified Trainers
Certification prep at 360DT is delivered live by Microsoft Certified Trainers, Anthropic Authorized Instructors and a Microsoft Business Applications MVP — our full international faculty.
Naga Nandhini
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Full stack lead and GenAI builder with 10+ years of training behind 50+ corporate batches for TCS, HCL, Perficient, Renault Nissan and Saint-Gobain. He ships real RAG and local-LLM systems with Ollama, FastAPI and Spring Boot, then teaches exactly how they work.
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NVIDIA Authorized Instructor and AI Data Specialist
Passionate AI and data evangelist with over 10 years of experience across machine learning, MLOps, and cloud native technologies. Dedicated to delivering impactful, engaging enterprise training programs.
Florian Garcia Compte
Cisco Certified Systems Instructor (CCSI)
Partner Director at AVAANZA FORMACION, a Cisco Learning Partner based in Madrid. Certified Cisco Systems Instructor (CCSI No. 21053) specializing in Cisco Data Center (ACI/SDN), Nexus, and Wireless, delivering official Cisco certification courses including CCNA, CCNP Enterprise, and CCNP Data Center.
MLOps Engineer Program — AI-300 plus Claude certification
The 360DT MLOps Engineer Program covers both halves of AI-300 — Azure ML, MLflow, CI/CD and drift monitoring on the MLOps side, Foundry, evaluators and RAG optimisation on the GenAIOps side — and pairs it with Anthropic’s Claude Certified Architect – Foundations. Built for the discipline Microsoft now calls AIOps.
Explore the MLOps Engineer Program
AI-300 frequently asked questions
What certification does AI-300 earn?
Microsoft Certified: Machine Learning Operations Engineer Associate. The exam title is different — “Operationalizing Machine Learning and Generative AI Solutions” — which causes a lot of confusion. Put the credential name on your CV and LinkedIn, not the exam title.
Is AI-300 about traditional ML or generative AI?
Both, almost equally. Grouping the five domains by discipline gives roughly 45% classic MLOps (model lifecycle plus MLOps infrastructure) and roughly 47% GenAIOps (GenAIOps infrastructure, quality assurance, and optimisation) at domain midpoints. Microsoft calls the combined discipline AIOps. Whichever side you come from, budget serious time for the other.
Do I need DevOps experience for AI-300?
Yes. Microsoft’s audience profile asks for a data science background plus an entry-level understanding of DevOps, naming GitHub Actions, CLIs and infrastructure as code with Bicep and Azure CLI. These appear directly in the skills measured, so they are examined rather than merely assumed.
What is the AI-300 passing score and duration?
700 out of 1000 on a scaled score, in 120 minutes. The exam is proctored through Pearson VUE and may include interactive components. You can retake it 24 hours after a first failure.
Is there an official AI-300 practice test?
Yes. Microsoft has published a Practice Assessment for AI-300 on AI Skills Navigator, and you need to be signed in to launch it. That is a point of difference from AI-200, which has no practice assessment available yet.
AI-300 or AI-103 — which should I take?
AI-103 is about building AI features and agents on Microsoft Foundry. AI-300 is about operating models in production — training pipelines, endpoints, drift, evaluation harnesses and cost monitoring, across both classic ML and generative AI. If you build the feature, AI-103. If you keep it running and prove it still works, AI-300.
How much salary hike can AI-300 give me?
The certificate itself carries no salary. The specialisation does: AI specialists are reported to command a 25–40% premium over standard cloud developers, and agentic AI engineering a 30–50% premium over generalist software engineering. Applied to Indian bands at the midpoint, that is roughly +₹2.5 L at entry, +₹5 L at mid, +₹9 L at senior and +₹12 L at lead level. Modelled figures for direction, not a quotation or a guarantee.
Does AI-300 expire?
Yes. Microsoft associate, expert and specialty certifications expire annually. Renewal is free through an online assessment on Microsoft Learn, taken within the renewal window before expiry.
Sources and further reading
- Microsoft Learn — Microsoft Certified: Machine Learning Operations Engineer Associate (credential name, 120-minute duration, proctored, English only, practice assessment on AI Skills Navigator, retake policy)
- Microsoft Learn — Study guide for Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions (audience profile, five skills-measured domains and weightings, 700 passing score, annual renewal)
- Published 2026 Azure AI engineer compensation data — US average ~$111,000, range $90,000–$129,500, top earners ~$145,000+
- Glassdoor India (July 2026) — AI Architect/Engineer average ~₹35 L, typical range ~₹20.75–47.75 L
- Independent 2026 industry reporting on the AI-specialist pay premium (25–40%) and agentic AI engineering premium (30–50%)
360 Digital Transformation is an Anthropic Partner. Microsoft certification exams are administered by Microsoft through Pearson VUE and are not included in programme tuition. Exam details are accurate as of 20 August 2026; always confirm current format, pricing and skills measured on Microsoft Learn before booking.
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