Download Microsoft : AI-300 Questions & Answers as PDF & Test Software
Updated: Oct 01, 2026
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Microsoft AI-300 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Passing Score: | 700/1000 |
| Available Languages: | English, Arabic (Saudi Arabia), Japanese, French, Italian, Chinese (Simplified), German, Chinese (Traditional), Korean, Russian, Portuguese (Brazil), Spanish, Indonesian (Indonesia) |
| Exam Price: | $165 USD |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100-120 |
| Exam Format: | Drag and drop, Case study, Build list, Multiple response, Multiple choice |
| Certificate Validity Period: | 1 year (renewable) |
| Sample Questions: | Microsoft AI-300 Sample Questions |
| Exam Way: | Online (proctored via Pearson VUE) or at a Pearson VUE testing center |
| Pre Condition: | Candidates should have subject matter expertise in setting up infrastructure for MLOps and GenAIOps solutions on Azure, with experience in training, deploying, and maintaining ML models using Azure Machine Learning and generative AI applications using Microsoft Foundry. No formal prerequisite exam is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-300 |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
An Azure AI Hub is the top-level governance container in Microsoft Foundry: it holds shared connections to Azure OpenAI, Azure AI Search, Azure Storage, and other services; it defines network isolation policies; it manages billing and quota at the organizational level. Multiple teams share these resources without each team needing to configure their own connections or negotiate quota independently. An Azure AI Project sits inside the Hub and provides team-level isolation: each project has its own experiments, deployments, prompt flows, evaluations, and fine-tuning jobs, all governed by the Hub ' s shared infrastructure. Different teams get their own project with independent access controls via RBAC, while the platform team manages the shared Hub.
This pattern eliminates redundant resource configurations across teams while maintaining clear team-level boundaries - the correct structure for centralized governance with team isolation.
Microsoft Learn Reference Topic: Microsoft Azure AI Foundry hub and project architecture - Centralized governance and team isolation
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files. What should you do?
- A. Move the file named. gift ignore to the. /outputs directory.
- B. Add the contents of the file named. amlignore to the file named. gift ignore.
- C. Copy the contents of the file named. gift ignore to the file named. amlignore.
- D. Move the file named. gift ignore to the. /logs directory.
Correct Answer: C 🗳️
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.wmdows.net/data1.
You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.
You need to select the parameters of the Azure Blob Datastore class that will point to the target datastore and authorize access to it.
Which parameters should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
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