もしあなたはまだMicrosoftのAI-300試験に合格するのために悩まればGoShikenは今あなたを助けることができます。GoShikenは高品質の学習資料をあなたを助けて優秀なMicrosoftのAI-300会員の認証を得て、もしあなたはMicrosoft AI-300の認証試験を通して自分を高めるの選択を下ろして、GoShikenはとてもよい選択だと思います。
| 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 | - Large language model integration
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
AI-300試験問題の更新を1年以内にクライアントに無料で提供し、1年後にクライアントは50％の割引を受けることができます。クライアントが古いクライアントの場合、一定の割引を享受できます。当社Microsoftの専門家は、毎日AI-300ガイドトレントを更新し、AI-300スタディガイドの最新の更新をクライアントに提供します。私たちはクライアントに割引を提供し、彼らがより少ないお金を使うようにします。あなたが古いクライアントである場合、あなたは特別割引を享受することができますので、お金を節約することができます。したがって、AI-300テストトレントを購入することは非常に価値があります。
質問 # 94
You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?
正解：B
解説：
The following custom role can do everything in the workspace except for the following actions:
It can ' t create or update a compute resource.
It can ' t delete a compute resource.
It can ' t add, delete, or alter role assignments.
It can ' t delete the workspace.
To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named " Data Scientist Custom " scoped at a specific workspace level:
data_scientist_custom_role.json :
{
" Name " : " Data Scientist Custom " ,
" IsCustom " : true,
" Description " : " Can run experiment but can ' t create or delete compute. " ,
" Actions " : [ " * " ],
" NotActions " : [
" Microsoft.MachineLearningServices/workspaces/*/delete " ,
" Microsoft.MachineLearningServices/workspaces/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/delete " ,
" Microsoft.Authorization/*/write "
],
" AssignableScopes " : [
" /subscriptions/ < subscription_id > /resourceGroups/ < resource_group_name > /providers/Microsoft.
MachineLearningServices/workspaces/ < workspace_name > "
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles
質問 # 95
An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
正解：C
解説：
To ensure an Azure Machine Learning (AML) workspace handling sensitive data is not accessible from the public internet, you must disable the Public Network Access flag and implement Private Endpoints. This configuration creates a private link between your Azure Virtual Network (VNet) and the workspace, ensuring traffic never traverses the public internet.
Reference:
https://www.azadvertizer.net/azpolicyadvertizer/438c38d2-3772-465a-a9cc-7a6666a275ce.html
質問 # 96
You manage an Azure Machine learning workspace named workspace1.
You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
You need to add the instantiated compute instance to workspace 1.
What should you use?
正解：C
質問 # 97
DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each 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 # 98
You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?
正解：B
解説：
You can use a simulator or an LLM-as-a-judge pipeline to generate synthetic interaction data for fine-tuning. This technique is standard practice for maintaining strict data privacy while training models on specific business tasks.
Here is how to effectively structure and execute a synthetic data generation pipeline for Microsoft Azure AI Foundry (formerly Azure AI Studio).
Generation Methods
Persona-Driven Simulation: Prompt one LLM to act as a customer and another as your support agent to generate multi-turn dialogues.
Seed Data Expansion: Feed 10-20 hand-written, compliant examples into an LLM and instruct it to generate hundreds of diverse variations.
Schema-Based Evolution: Define variables (e.g., product types, user intents, sentiment levels) and programmatically combine them into prompt templates for an LLM to flesh out.
Reference:
https://www.digitaldividedata.com/blog/synthetic-data-generation-in-gen-ai
質問 # 99
......
お客様に最高のサービスを提供するというコンセプトに沿って、当社は専任のサービスチームと成熟した思慮深いサービスシステムを構築しました。クライアントがAI-300トレーニング資料を購入する前に無料トライアルを提供するだけでなく、販売後に相談サービスも提供します。クライアントがAI-300認定ガイドについて体系的かつ的を絞った学習を行えるように、複数の機能を提供しています。したがって、クライアントは間違いなくAI-300試験の教材を信頼できます。
AI-300対策学習: https://www.goshiken.com/Microsoft/AI-300-mondaishu.html