經過相關的研究材料證明，通過Microsoft的AI-300考試認證是非常困難的，不過不要害怕，我們KaoGuTi擁有經驗豐富的IT專業人士的專家，經過多年艱苦的工作，我們KaoGuTi已經編譯好最先進的Microsoft的AI-300考試認證培訓資料，其中包括試題及答案，因此我們KaoGuTi是你通過這次考試的最佳資源網站。不需要太多的努力，你將獲得很高的分數，你選擇KaoGuTi Microsoft的AI-300考試培訓資料，對你考試是非常有幫助的。
| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Security and governance
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
為了讓生活過得更好些，參加 AI-300 認證考試獲取 Microsoft 認證是每位選擇IT行業的工作人員必經之路。只有獲取了公司要求的這張證書既可獲得加薪和升遷的機會。而 Microsoft 在考古題考試方面的雄厚實力源於業界企業的大力支持。數千家公司均依託 Microsoft 標準來提供一個可靠的員工業績評估。此外，數十家擁有自己考古題專案的公司也非常信賴 Microsoft 的 AI-300 考古題，以確保員工具備扎實的技能功底。此舉可以為公司節省大量的時間和開銷。
問題 #103
You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.wmdows.net/data1.
You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.
You need to select the parameters of the Azure Blob Datastore class that will point to the target datastore and authorize access to it.
Which parameters should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
問題 #104
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?
答案：B
解題說明：
To implement an automated evaluation step in GitHub Actions for Microsoft Foundry AI, you can use the Microsoft Foundry Evaluation GitHub Action (or the Azure AI Evaluation SDK).
This setup allows you to run both built-in metrics (like groundedness or coherence) and custom safety checks, then fail the build if scores fall below your defined thresholds.
Implementation Steps
1. Define Your Evaluators
You need to configure which metrics to use. Microsoft Foundry supports two main types:
Built-in Metrics: Pre-trained models that score quality (coherence, fluency) and safety (hate, violence, self-harm).
Custom Metrics: Python-based evaluators you define to check domain-specific requirements.
2. Configure the GitHub Actions Workflow
Create a .yml file in your .github/workflows directory. This workflow will:
Trigger on a pull request or commit.
Authenticate with Azure/Foundry.
Run Evaluation using the microsoft/ai-agent-evals action.
Enforce Thresholds to block the release if quality is insufficient.
Key Components for "Block Release" Logic
To ensure the release is blocked, your workflow must include a gating step that interprets the evaluation results:
Reference:
https://learn.microsoft.com/en-us/training/modules/automated-evaluation-genaiops
問題 #105
You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point
答案：B,E
問題 #106
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
答案：A
解題說明：
Correct:
* Create an environment.
Incorrect:
* Delete the Python 3.6 - AzureML kernel.
* Delete the Python 3.8 - AzureML kernel.
Note:
Before you can add a new Jupyter kernel on an Azure Machine Learning compute instance terminal, you must create a Conda environment.
Required Workflow
To officially provision and expose the new kernel to your Azure Machine Learning studio Notebooks, you need to execute the following full process from your terminal session:
Create the environment: Provision a new isolated environment (e.g., using conda create -n newenv python=3.10).
Activate the environment: Run conda activate newenv.
Install dependencies: Add the required ipykernel package using conda install ipykernel or pip install ipykernel.
Register the kernel: Bind the new environment configuration to the global Jupyter directory by running:
python -m ipykernel install --user --name newenv --display-name "My New Kernel" Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-access-terminal
問題 #107
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?
答案：C
問題 #108
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