在短短幾年內，Microsoft AI-103 認證考試已經成為比較有影響力電腦能力認證考試。然而如何簡單順利地通過Microsoft AI-103認證考試？我們的NewDumps在任何時間下都可以幫您快速解決這個問題。我們在NewDumps中為您提供了可以成功通過AI-103認證考試的培訓工具。AI-103認證考試培訓工具的內容是由IT行業專家帶來的最新的考試研究材料組成
| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
獲得AI-103認證是眾多IT人員職業生涯的成功保證，而NewDumps網站中的AI-103題庫學習資料可以幫助您做到這一點。只要您支付您想要的考古題，您就能馬上得到它，在通眾多使用過本題庫產品的客戶回饋中，證明Microsoft AI-103考古題是值得信賴的。AI-103題庫可以確保考生順利通過考試，大家還有什么理由不選擇呢？快將AI-103考古題加入購物車吧，您絕對不會后悔的！
問題 #58
You have a Python application that sends customer support transcripts to Azure Language in Foundry Tools by using the REST API. The transcripts are in English and can contain customer names, email addresses, and phone numbers.
You need to store a redacted transcript of each conversation. The support workflow must redact only customer names and leave other entity types unchanged.
How should you complete the analyze-text request body? 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:
The operation must use PII Entity Recognition because the required output is a redacted version of the input based on personally identifiable information.
The " kind " property should therefore be set to " PiiEntityRecognition " . The piiCategories parameter restricts which sensitive categories participate in the analysis. Setting it to only [ " Person " ] ensures that recognized customer names are selected while other PII categories-such as PhoneNumber and email-related categories-are not included in the requested PII category set.
This distinction is important because the service supports numerous PII entity categories. Without category restriction, additional sensitive information could be recognized and redacted, which would violate the requirement to leave email addresses and phone numbers unchanged.
EntityRecognition is general Named Entity Recognition and is designed to identify broad entities such as Person, Organization, Location, and DateTime; it is not the specialized redaction operation required here.
LanguageDetection only identifies language.
The resulting PII response can provide redacted text and entity metadata that the application stores or audits.
Study Guide alignment: configure detection of sensitive content and implement solutions that extract entities and structured information from text . Microsoft Learn
問題 #59
You train an Azure Custom Vision object detection model to identify a company ' s products by using the Retail domain.
You plan to deploy the model as part of a mobile app for Android phones.
You need to prepare the model for deployment.
Which three 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.
答案：
解題說明：
Explanation:
* Change the model domain to a compact domain.
* Retrain the model.
* Export the model.
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
A Custom Vision model can be packaged for local mobile inference only when it is trained by using an exportable compact domain .
The current Retail-domain model is intended for its hosted Custom Vision scenario and cannot simply be copied into an Android application. Therefore, first change the model/project domain to the appropriate compact object-detection domain, such as General (compact) where applicable.
Second, retrain the model . The existing Retail iteration was trained using a different underlying model
/domain configuration. Retraining creates a new compact iteration that is optimized for export and edge inference.
Third, export the compact model using an Android-compatible format supported by Custom Vision and the app ' s inference runtime. Depending on the available project/export options, this can involve formats such as TensorFlow/TensorFlow Lite or ONNX.
Testing is useful before production deployment but does not replace any of the three steps required to transform the hosted Retail model into an exportable mobile model.
The correct lifecycle is therefore:
change domain # retrain # export
Study Guide alignment: implement object/component detection and prepare vision solutions for appropriate local or mobile execution environments .
問題 #60
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
答案：A
解題說明：
To resolve the HTTP 403 Forbidden errors when making inference calls via the Azure OpenAI v1 API and DefaultAzureCredential, users must be assigned the Cognitive Services OpenAI User built-in Azure RBAC role.
Why This Happens
Running az login successfully authenticates the user with Microsoft Entra ID, but it does not grant data-plane access permissions. By default, standard control-plane roles (like Reader or Foundry User) only allow users to view project metadata or manage settings, not send prompts to the model deployment endpoint itself.
Recommended Role Definition
Role Name: Cognitive Services OpenAI User
Permissions Granted: This role provides the absolute minimum privileges required to execute chat completions, embeddings, and general inference tasks (Microsoft.CognitiveServices/accounts/OpenAI/deployments/search/action and Microsoft.CognitiveServices/accounts/OpenAI/deployments/causalLanguageModeling/action). It does not allow users to deploy new models, view access keys, or alter configurations Scope Placement: Assign this role to the users (or a Microsoft Entra ID Group) at the Azure OpenAI resource level or the Resource Group level containing your Microsoft Foundry infrastructure.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/managed-identity
問題 #61
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
答案：A
問題 #62
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal Al generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure protected material detection.
Does this meet the goal?
答案：A
解題說明：
The solution does not meet the goal. Protected material detection is intended to identify large language model output that matches known protected text or code, such as copyrighted text, selected web content, song lyrics, articles, recipes, or code. Microsoft describes protected material detection as a control for preventing AI- generated content from reproducing known protected material, not as a control for image safety or prompt injection.
The stated risk has two parts: users can upload unsafe images, and users can embed hidden instructions in images to manipulate the model. Unsafe image uploads require image moderation, because Azure AI Content Safety provides image APIs that detect harmful content across modalities and can support blocking decisions by harm category and severity. Hidden instructions extracted from images are indirect prompt injection or document attacks; Microsoft Prompt Shields are the capability designed to detect user prompt attacks and document attacks, including harmful instructions embedded in third-party content.
Therefore, protected material detection alone does not mitigate either primary risk. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and protected material detection.
問題 #63
......
NewDumps的IT專家團隊利用他們的經驗和知識不斷的提升考試培訓材料的品質來滿足考生的需求，保證考生順利地通過第一次參加的Microsoft AI-103認證考試。通過購買NewDumps的產品你總是能夠更快得到更新更準確的考試相關資訊。並且NewDumps的產品的覆蓋面很廣，可以為很多參加IT認證考試的考生提供方便，而且準確率100%。它能給你100%的信心，讓你安心的參加考試。
AI-103考古題介紹: https://www.newdumpspdf.com/AI-103-exam-new-dumps.html