Exam guide for cloud switchers · Associate

AI-300 for AWS engineers: Operationalizing Machine Learning and Generative AI Solutions

AI-300 is the Machine Learning Operations Engineer Associate exam, the closest successor in scope to the retired DP-100.

It covers MLOps on Azure Machine Learning (workspaces, MLflow, pipelines, endpoints, monitoring) and GenAIOps on Foundry (model deployment, prompt versioning, evaluation, observability, RAG and fine-tuning). It maps closely to SageMaker AI plus Bedrock operations.

Last verified: Oct 27, 2026 against the official AI-300 study guide · Edit on GitHub · Report an error

Exam at a glance

Exam	AI-300: Operationalizing Machine Learning and Generative AI Solutions
Certification	Microsoft Certified – Machine Learning Operations Engineer Associate (Associate)
Status	current MLOps and GenAIOps infrastructure on Azure (IaC with Bicep/CLI, GitHub Actions). Closest successor in scope to the retired DP-100.
Skills measured as of	Not stated on the study guide
Passing score	700 or greater (per the study guide)
Closest AWS cert	MLA-C01

Exam prices vary by country and are set on the exam page, so none are quoted here. Microsoft doesn't publish pass rates.

Who it's for

AWS ML engineers (the MLA-C01/MLA-C02 audience) running SageMaker in production.
Former DP-100 candidates. DP-100 is retired.
Platform engineers automating ML and GenAI infrastructure with Bicep and GitHub Actions.

Domains and how they map to AWS

Domain (official)	Weight	Compare pages
Design and implement an MLOps infrastructure	15–20%	4
Implement machine learning model lifecycle and operations	25–30%	10
Design and implement a GenAIOps infrastructure	20–25%	4
Implement generative AI quality assurance and observability	10–15%	2
Optimize generative AI systems and model performance	10–15%	3

1. Design and implement an MLOps infrastructure 15–20%

Official skill areas: Create and manage resources in a Machine Learning workspace · Create and manage assets in a Machine Learning workspace · Implement IaC for Machine Learning

From AWS: SageMaker domains and Studio map to the Azure Machine Learning workspace, datastores, compute targets, environments, components and registries, deployed with Bicep and GitHub Actions.

Compare pages to study (4):

2. Implement machine learning model lifecycle and operations 25–30%

Official skill areas: Orchestrate model training · Implement model registration and versioning · Deploy machine learning models for production environments · Monitor and maintain machine learning models in production

From AWS: SageMaker Experiments/MLflow, Autopilot, training jobs, Pipelines, Model Registry, endpoints and Model Monitor map to MLflow tracking, AutoML, command and sweep jobs, Azure ML pipelines, MLflow model registration, managed online and batch endpoints, and data drift monitoring.

Compare pages to study (10):

3. Design and implement a GenAIOps infrastructure 20–25%

Official skill areas: Implement Foundry environments and platform configuration · Deploy and manage foundation models for production workloads · Implement prompt versioning and management with source control

From AWS: Bedrock model access and provisioned throughput map to Foundry resources and projects, serverless API vs managed deployments, and PTUs. Prompt management maps to prompts versioned in Git.

Compare pages to study (4):

4. Implement generative AI quality assurance and observability 10–15%

Official skill areas: Configure evaluation and validation for generative AI applications and agents · Implement observability for generative AI applications and agents

From AWS: Bedrock evaluations and AgentCore observability map to Foundry evaluations (groundedness, relevance, safety) and continuous monitoring and tracing.

Compare pages to study (2):

5. Optimize generative AI systems and model performance 10–15%

Official skill areas: Optimize retrieval-augmented generation (RAG) performance and accuracy · Implement advanced fine-tuning and model customization

From AWS: Knowledge Bases tuning and Bedrock model customization map to RAG tuning (chunking, embeddings, hybrid search) and Foundry fine-tuning with synthetic data.

Compare pages to study (3):

Concepts that trip AWS engineers up

MLflow is the native API

Azure ML workspaces are MLflow-compatible tracking servers, and MLflow models are the preferred registration format. SageMaker offers managed MLflow as one option among several. Here it is the default.

Endpoints have deployments

A managed online endpoint holds one or more deployments with a traffic split, which is how blue-green and canary rollouts are done. This is close to SageMaker production variants, but with different terms and commands.

Registries are cross-workspace

Azure ML registries share models, environments and components across workspaces and regions. That's broader than a SageMaker Model Registry model package group.

About half the exam is GenAIOps

The three Foundry domains (GenAIOps infrastructure, quality and observability, and RAG and fine-tuning optimization) add up to 40–55% of the weight. That's much more GenAI than DP-100 or MLA-C01 had.

Closest AWS certification

AWS Certified Machine Learning Engineer – Associate MLA-C01 Retiring: Same MLOps role. MLA-C01 is being replaced by MLA-C02. Being updated to MLA-C02 (beta registration opened Sep 1, 2026). Last day for MLA-C01 in English was Sep 28, 2026; Korean, Japanese, and Simplified Chinese remain until MLA-C02 GA.

Verification notes

The AI-300 study guide did not show a "skills measured as of" date when checked.

FAQ

What does AI-300 cover, and how is it weighted?

AI-300 (Operationalizing Machine Learning and Generative AI Solutions) has 5 domains: Design and implement an MLOps infrastructure (15–20%); Implement machine learning model lifecycle and operations (25–30%); Design and implement a GenAIOps infrastructure (20–25%); Implement generative AI quality assurance and observability (10–15%); Optimize generative AI systems and model performance (10–15%). Weights are from the official Microsoft Learn study guide.

What is the closest AWS certification to AI-300?

AWS Certified Machine Learning Engineer – Associate (MLA-C01): Same MLOps role. MLA-C01 is being replaced by MLA-C02.

Is AI-300 a current exam?

As of Oct 27, 2026, Microsoft Learn lists AI-300 as current. MLOps and GenAIOps infrastructure on Azure (IaC with Bicep/CLI, GitHub Actions). Closest successor in scope to the retired DP-100.