Microsoft MLOps Engineer Associate (AI-300)

Vendor: Microsoft · Last verified: 4 October 2026 · How we rate

Fee $165 (US price)Level IntermediateAccess Open to anyoneDifficulty 4/5

1. The quick verdict

2. Our ratings

Criterion	Score	Why
Difficulty (5 = hardest)	●●●●○ 4/5	Spans ML lifecycle, GenAIOps and infrastructure as code.
Cost burden, including fee, prep and retake risk (5 = most expensive)	●●○○○ 2/5	Mid-range fee and longer prep; free annual renewal.
Career value today (5 = highest)	●●●●○ 4/5	MLOps and GenAIOps skills are in demand, and this is Microsoft’s main credential for them.
Skill transfer beyond this vendor (5 = most portable)	●●●○○ 3/5	MLflow, CI/CD and evaluation practices transfer; Azure ML and Foundry specifics do not.
Hands-on experience required (5 = must have built real things)	●●●●○ 4/5	Assumes real pipelines and deployments.
Ease of access (5 = anyone can register)	●●●●○ 4/5	Open to anyone, but English only.

3. At a glance

Field	Details
Level	Associate (Intermediate)
Access	Open to anyone
Exam fee	US$165 in the United States for Associate exams; varies by country. Confirm at checkout.
Retake policy	Fee each attempt. Wait 24 hours after the 1st fail, then 14 days. Max 5 attempts in 12 months.
Format	Pearson VUE, online proctored or test centre. 120 minutes, with interactive components. Microsoft Learn is available during the exam. English only.
Passing score	700 on a 1,000 scale
Coding needed	Reading Python, YAML pipelines, Bicep and CLI commands.
Formal prerequisites	None
Realistic prerequisites	A data science background with Python, plus entry-level DevOps (GitHub Actions, CLIs) and hands-on Azure Machine Learning and Foundry.
Validity & renewal	1 year. Renew free with an online, open-book assessment in the 6 months before expiry.
Estimated prep time	Running Azure ML in production now: 30–45 hours. Data scientist new to MLOps: 60–80 hours.
Official exam guide	AI-300 study guide (Microsoft Learn)

Prep-time estimates are our own judgment, not a Microsoft figure.

4. What the exam tests

Domain	Weight	What it means in plain language
Implement machine learning model lifecycle and operations	25–30%	Training with MLflow and AutoML, registering and versioning models, deploying endpoints, and detecting drift.
Design and implement a GenAIOps infrastructure	20–25%	Setting up Foundry, deploying foundation models, and versioning prompts.
Design and implement an MLOps infrastructure	15–20%	Workspaces, data and environment assets, and infrastructure as code with GitHub, Bicep and Azure CLI.
Implement generative AI quality assurance and observability	10–15%	Evaluating groundedness, relevance and safety, and monitoring latency, throughput and cost.
Optimize generative AI systems and model performance	10–15%	Tuning RAG, fine-tuning models and generating synthetic data.

5. Who should take it, and who shouldn’t

6. What to expect after passing

• What it signals: You can take ML and generative AI models from experiment to monitored production on Azure.
• What it doesn’t prove: Research-level ML, or agent application design.
• Where it’s asked for: MLOps, ML engineer and AI platform roles on Azure; it is also one of the Associate certifications that unlock AB-100.
• Natural next step: AI-500 for agent systems (after AI-103), or deeper platform engineering.

7. How to prepare

• Official: The AI-300 study guide, the exam sandbox, the practice assessment on AI Skills Navigator, and Azure Machine Learning and Foundry documentation.
• Practice: Use scenario questions on choosing deployment types, evaluation metrics and pipeline designs. Avoid DP-100 material, which lacks GenAIOps.

Hands-on:

1. Train a model with MLflow tracking, register it, and deploy a managed online endpoint (ML lifecycle).
2. Automate the workspace and pipeline with Bicep and GitHub Actions (MLOps infrastructure).
3. Deploy a Foundry model, version its prompt, and run groundedness and safety evaluations (GenAIOps, Quality assurance).

8. Compare with

• AWS Certified Machine Learning Engineer – Associate (MLA-C01), $150: AWS’s equivalent, built around SageMaker.
• Microsoft Azure AI Apps and Agents Developer Associate (AI-103): Building AI apps and agents rather than operating ML systems.
• NVIDIA Generative AI LLMs Professional (NCP-GENL), $200: The LLM-specific counterpart: fine-tuning, distributed training and GPU optimisation, not tied to one cloud.

See all Microsoft guides → · Compare all AI certifications →

9. FAQ

Is AI-300 the replacement for DP-100?

In practice, yes. The Azure Data Scientist Associate (DP-100) is retired, and AI-300 covers the production side of ML plus generative AI operations.

How much DevOps do I need?

Entry level: GitHub Actions, the Azure CLI and Bicep templates. The exam tests applying them to ML pipelines.

Is the exam available in other languages?

Not yet; it is English only.

Sources

• Exam AI-300, Microsoft Learn: status, duration, language
• AI-300 study guide, Microsoft Learn: domains, weights, audience
• Azure Data Scientist Associate (retired), Microsoft Learn
• Exam retake policy, Microsoft Learn
• Renew your Microsoft certification, Microsoft Learn