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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Security and governance
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.
- A. Analyze error rates across the global cohort.
- B. Evaluate feature importance for prediction transparency.
- C. Analyze error rates across defined demographic cohorts.
- D. Measure endpoint latency under load.
- E. Validate inference schema compatibility.
Correct Answer: B,C 🗳️
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
- A. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
- B. Deploy the model lo Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
- C. Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.
- D. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.mode parameter to configure the authentication type.
Correct Answer: C 🗳️
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool 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:
Implement variants: LLM tool
Invoke a custom API: Python tool
For implementing variants , use the LLM tool . Microsoft documents that Prompt flow variants are supported specifically on LLM tool nodes. A variant represents an alternative configuration of the same node, such as different prompt text, temperature values, deployment settings, or other LLM parameters. This allows multiple prompt/model configurations to be tested without duplicating the entire flow, which directly minimizes development effort.
For invoking a custom API , use the Python tool . The Python tool executes custom Python logic within a Prompt flow node and can integrate with external or third-party services. Microsoft explicitly describes Prompt flow tools as supporting integration with third-party APIs and Python packages, and the Python tool can also consume a custom connection when authentication credentials are required. This makes it the appropriate choice for calling an API that is not covered by a built-in Prompt flow tool.
The Embedding tool is designed to generate vector embeddings for text and is therefore unrelated to either requirement. It does not provide variant functionality and is not the general-purpose mechanism for invoking a custom API.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Prompt flow tools, LLM variants, Python nodes, custom integrations, and flow experimentation.
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 have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the scoring_script parameter.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: B 🗳️
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