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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Security and governance
|
| Topic 2: Design and implement generative AI solutions | - Large language model integration
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
- A. Apply a blocklist.
- B. Generate synthetic interaction data.
- C. Configure content filters.
- D. Enable observability metrics.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
-
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The command deploys the resources into an existing resource group named rg-foundry-dev: Yes The system-assigned managed identity defined in the template will automatically be inherited by all Microsoft Foundry projects: No To deploy this template to a different subscription, you must modify the Bicep file to include a subscriptionId parameter: No The first statement is Yes . The command uses az deployment group create --resource-group rg-foundry-dev, which performs an Azure Resource Manager deployment at resource-group scope . Microsoft documents that the target resource group must already exist; if it does not, it must be created before running the resource- group deployment.
The second statement is No . The Bicep declaration assigns a system-assigned managed identity to the Foundry resource itself . A Microsoft Foundry project is a child resource and can have its own managed identity . Microsoft explicitly shows project creation with " identity " : { " type " : " SystemAssigned " } and separately discusses assigning permissions to a project ' s managed identity. Therefore, the parent ' s system- assigned identity is not automatically inherited as the identity of every project.
The third statement is No . Because targetScope = ' resourceGroup ' , the same Bicep template can be deployed to a resource group in another subscription without adding a subscriptionId parameter. Azure CLI supports the global --subscription argument, or the active subscription can be changed with az account set.
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
- A. UTF-8
- B. UTF-16
- C. ISO-8859-1
- D. ASCII
Correct Answer: A 🗳️
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You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Correct Answer:
Explanation:
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
