如果你正在準備 AI-300 考試，為 AI-300 認證做最後衝刺，又苦於沒有絕對權威的考試真題模擬。很多考生現在都用 Microsoft AI-300 考題作為參加AI-300 考試最快捷，最信任的方式。擺正好心態，認真閱讀准備好的 AI-300 考題，考試時心中不要慌，任何一場考試，都是與考生在進行心理戰的准備，遇到難的題目先不要去管，調整好心態准備應戰下一條題目。加上之前准備充足獲取 AI-300 認證應該是沒有問題的。
| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
現在有許多IT培訓機構都能為你提供Microsoft AI-300 認證考試相關的培訓資料，但通常考生通過這些網站得不到詳細的資料。因為他們提供的關於Microsoft AI-300 認證考試資料都比較寬泛，不具有針對性，所以吸引不了考生的注意力。
問題 #38
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?
答案：D
問題 #39
Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States.
Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
* Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
* Azure AI Search indexing curated analytical documents and reference materials
* A small set of Python-based training scripts maintained by data scientists
* Azure OpenAI Service with deployed foundational models
* A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
* Model training jobs are run manually from notebooks.
* Experiment tracking is inconsistent
* Model versions are registered without standardized metadata.
* Deployment is performed manually by data scientists, with limited rollback capability.
* The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
* Provide a conversational interface that answers analytics questions by using internal documents and datasets.
* Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
* Enable repeatable and auditable model training and deployment processes.
* Support experimentation to compare prompt strategies and fine-tuned models.
* Align the model with the ranked preferences and optimize behavior for the long term.
* Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
* Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
* Implement experiment tracking and model versioning for all training jobs.
* Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
* Deploy traditional machine learning models with support for staged rollout and rollback.
* Improve RAG-based solution output quality.
* Use the existing evaluation datasets that are based on real data with input-output pairs.
* Apply advanced fine-tuning techniques only when prompt engineering is insufficient Issues and Constraints Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities.
Hotspot Question
You need to deploy the RAG-based chat application that meets Fabrikam Inc.'s business and technical requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
問題 #40
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.
答案：
解題說明：
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
問題 #41
Drag and Drop Question
You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1.
You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone.
Which four actions should you perform in sequence?
答案：
解題說明：
Explanation:
Step 1: Create a compute instance
Git integration for Azure Machine Learning
Clone Git repositories into your workspace file system
Azure Machine Learning provides a shared file system for all users in the workspace. To clone a Git repository into this file share, we recommend that you create a compute instance (Step 1) & open a terminal (Step 2). Once the terminal is opened, you have access to a full Git client and can clone and work with Git via the Git CLI experience.
We recommend that you clone the repository into your user directory so that others will not make collisions directly on your working branch.
Step 2: Open a terminal window
Step 3: Generate a Secure Shell (SSH) key pair
Authenticate your Git Account with SSH
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C your_email@example.com
This creates a new ssh key, using the provided email as a label.
3. When you're prompted to "Enter a file in which to save the key" press Enter. This accepts the default file location.
4. Verify that the default location is '/home/azureuser/.ssh' and press enter. Otherwise specify the location '/home/azureuser/.ssh'.
5. At the prompt, type a secure passphrase. We recommend you add a passphrase to your SSH key for added security Step 4: Add a public key to account1.
Add the public key to Git Account
1. In your terminal window, copy the contents of your public key file. If you renamed the key, replace id_rsa.pub with the public key file name.
cat ~/.ssh/id_rsa.pub
2. Select and copy the SSH key output to your clipboard.
3. Next, follow the steps to add the SSH key to your preferred account type:
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration
問題 #42
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?
答案：D
解題說明：
The first action is to register the model in the Azure Machine Learning workspace . Microsoft's documented no-code workflow for generating Responsible AI insights in Azure Machine Learning studio explicitly starts by registering the model. After registration, you navigate to Models , select the registered model, and choose Create Responsible AI dashboard .
The scenario already specifies an MLflow-formatted regression model using tabular data , which aligns with the Responsible AI dashboard's supported model architecture. Microsoft states that models used by the Responsible AI components must be registered in Azure Machine Learning in MLflow format with an sklearn flavor , and the associated datasets must use supported tabular formats such as MLTable.
Option B is incorrect because model explanations are an optional dashboard component configured after selecting the registered model; they are not the prerequisite first step. Option C is unnecessary and counterproductive because MLflow is a supported format. Option D is also unnecessary: a Responsible AI dashboard evaluates a registered model and does not require deployment to a managed online endpoint first.
Microsoft also describes Responsible AI dashboards as being linked directly to registered models in the model registry.
Study Guide Reference: Implement machine learning model lifecycle and operations - model registration, MLflow model management, Responsible AI dashboards, model evaluation, and Azure Machine Learning studio.
問題 #43
......
因為Microsoft技術一直在快速發展，所以AI-300認證考試的試題也在不斷變化。因此， Fast2test的考古題也在一直更新。並且，如果你購買了Fast2test的資料，Fast2test將為你提供一年的免費更新服務。只要試題一更新，Fast2test馬上把最新版的資料發送給你。這樣就可以保證你隨時擁有最新版的資料。Fast2test不僅可以幫助你通過考試，還可以幫助你學習最新的知識。这样实惠的资料你千万不要错过。
最新AI-300考題: https://tw.fast2test.com/AI-300-premium-file.html