あなたはすぐAI-300試験に参加したいかもしれません。そうすれば、自分の能力を有る分野で証明できます。しかし、AI-300試験のために、どんな資料がいいですか。もちろん、AI-300問題集は一番いいです。AI-300問題集の内容は精確で、全面的です。AI-300問題集について、私たちはあなたのお問い合わせをお待ちします。
| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
今競争の激しいIT業界で地位を固めたいですが、Microsoft AI-300認証試験に合格しなければなりません。IT業界ではさらに強くなるために強い専門知識が必要です。Microsoft AI-300認証試験に合格することが簡単ではなくて、Microsoft AI-300証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。
質問 # 140
A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
What should you do?
正解：A
解説：
Configuring the workspace to use a managed identity (or a centralized service principal/workspace identity) is the correct approach to securely and centrally manage repository access without spreading personal access tokens.
No Developer Credentials: Developers do not need to generate, share, or store personal access tokens (PATs) on their local machines.
Centralized Management: Access permissions are bound to the cloud workspace resource rather than individual user accounts.
Reduced Credential Spreading: Eliminates the risk of leaked tokens when team members leave or compromise local workstations.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication
質問 # 141
You have a Microsoft Foundry project and a CSV file stored in Azure Blob Storage.
You plan to add the CSV file as the grounding data to the project for RAG by using the file data type.
You need to specify the URL schema to designate the blob location.
Which URL schema should you specify?
正解：D
解説：
wasbs (Windows Azure Storage Blob Secure) is the officially supported URL scheme used by the underlying Azure Machine Learning and Microsoft Foundry data infrastructure to securely access and locate storage files directly from an Azure Blob Storage account. A typical URI format looks like: wasbs://<containername>@<accountname>.blob.core.windows.net/<path_to_data>.csv.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
質問 # 142
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解：
解説：
Explanation:
To ensure runtime dependencies are consistent and reusable, first create a conda.yaml or requirements.txt file that lists all Python packages and system libraries required by your training code - this file is the single source of truth for your runtime. Next, create an Environment object using the Azure ML Python SDK v2 with a name and reference to the conda.yaml file, specifying the base Docker image. Then register the Environment by calling ml_client.environments.create_or_update, which publishes it to the workspace registry with an auto-incremented version. Finally, reference the registered environment by name and version in all pipeline job steps. Azure ML will build or retrieve the cached Docker image and use it as the execution container. This approach means updating dependencies only requires modifying the conda.yaml and registering a new version - training code remains unchanged.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning environments - Reusable curated environments
質問 # 143
A company ' s platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
正解：A、D
解説：
Microsoft ' s Azure AI Foundry documentation defines built-in roles scoped to the resource hierarchy. The Azure AI Administrator role grants permissions to manage the Azure AI Hub resource itself - including network settings, shared connections, quota, and governance configuration - which is appropriate for platform engineers who configure resource-level settings. The Azure AI Developer role grants permissions to create, update, and manage project-level assets such as deployments, prompt flows, fine-tuning jobs, and evaluations, without access to resource-level configuration. This precisely matches the developer requirement and enforces least privilege. Disabling Entra ID authentication (option B) violates security policy and removes the identity-based access control that makes RBAC possible. Sharing a single API key (option D) violates the least-privilege principle - all users would have identical, undifferentiated access with no audit trail.
Microsoft Learn Reference Topic: Manage access to Azure AI Foundry - Built-in roles: Azure AI Administrator and Azure AI Developer
質問 # 144
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
正解：C
解説：
To share validated assets between development and production workspaces while maintaining centralized governance and version control, you should use Azure Machine Learning Registries.
Microsoft Learn
Unlike standard workspaces, registries are workspace-agnostic. They act as a central catalog that decouples assets from individual environments, allowing you to "promote" a model or environment from Dev to Prod without manual duplication or data drift.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries- mlops
質問 # 145
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