Jpshiken提供した商品の品質はとても良くて、しかも更新のスピードももっともはやくて、もし君はMicrosoftのAI-300の認証試験に関する学習資料をしっかり勉強して、成功することも簡単になります。
| Section | Objectives |
|---|---|
| Topic 1: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 2: Design and implement generative AI solutions | - Large language model integration
|
| Topic 3: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 4: Operationalizing machine learning solutions | - ML lifecycle management
|
現在の社会で、AI-300試験に参加する人がますます多くなる傾向があります。市場の巨大な練習材料からAI-300の学習教材を手に入れようとする人も増えています。 私たちのAI-300試験問題を利用し、ほかの資料が克服できない障害を克服できます。 多くの受験者は、私たちのAI-300練習試験をすることに特権を感じています。 そして、私たちのウェブサイトは、市場でのとても有名で、インターネット上で簡単に見つけられます。
質問 # 33
You manage an Azure Machine Learning workspace named workspace!.
You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2.
You must transform the Python code into a YAML specification that can be processed by the pipeline service.
You need to import the Python library that provides the transformation functionality.
Which Python library should you import?
正解：D
質問 # 34
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least- privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism 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.
正解：
解説：
Explanation:
Managed Identity is Azure ' s solution for eliminating stored secrets and API keys from application configurations. When a Microsoft Foundry resource or its dependent compute has a system-assigned or user- assigned managed identity, it can authenticate to Azure services such as Storage, Key Vault, and Cognitive Services using Azure Entra ID tokens that are automatically issued and rotated by Azure. There are no keys to store, rotate, or accidentally expose in code or configuration files, directly satisfying the requirement to authenticate without stored credentials. Role-Based Access Control (RBAC) is the Azure governance mechanism for scoping permissions. By assigning specific built-in roles to the managed identity or to user and group principals at the correct scope - subscription, resource group, resource, or project level - the organization can enforce least-privilege access across different projects and shared resources.
Microsoft Learn Reference Topic: Configure managed identities and RBAC for Microsoft Azure AI Foundry resources
質問 # 35
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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.
正解：
解説：
質問 # 36
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)
正解：B、E
解説：
B and C provide the strongest architecture for reusable and predictable prompts. Separating persistent application instructions from dynamic user input establishes a clear instruction hierarchy . The stable portion of the prompt can define application behavior while channel-specific or request-specific content remains variable. This reduces coupling between user wording and the behavioral rules of the assistant and makes the prompt easier to reuse, version, test, and modify.
The system prompt should establish the model ' s role, behavioral boundaries, tone, communication style, response format, and other persistent rules. Microsoft specifically describes system messages as high-priority instructions used to define an assistant ' s role and boundaries and to set tone and communication style.
Fine-tuning for every conversational variation is unnecessarily expensive and contradicts the requirement to remain adaptable without retraining. Increasing temperature increases randomness and therefore works against consistent responses. Although repeating important instructions can sometimes mitigate recency effects, Microsoft treats this as an optional prompt-engineering technique to experiment with, not the primary architectural technique for producing reusable prompts.
Study Guide Reference: Design and implement a GenAIOps infrastructure - design and develop prompts, prompt management, prompt variants, system instructions, and reusable prompt structures.
質問 # 37
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
正解：B、C、E
解説：
To reset the state of a Python SDK v2 notebook in Azure Machine Learning Studio, you can perform any of the following three individual actions:
Stop the current kernel
Reset the compute instance
Change the current kernel
Each of these steps acts as a standalone solution to clear the current runtime memory, variables, and overall execution state of your notebook.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks
質問 # 38
......
AI-300試験を恐れることはありません。AI-300準備資料があなたの現在の生活を変えるのに役立つと信じています。 AI-300試験に合格して認定を取得できれば、近い将来新しい有意義な生活を始めることができます。したがって、AI-300模擬試験の準備をすることは非常に重要です。AI-300認定ガイドにもっと注意を払う必要があります。また、AI-300試験の質問は、認定を取得するためのすべての手助けとなります。
AI-300日本語版試験解答: https://www.jpshiken.com/AI-300_shiken.html