Fast2test의 Microsoft AI-103덤프를 구매하기전 우선 pdf버전 덤프샘플을 다운받아 덤프문제를 공부해보시면Fast2test덤프품질에 신뢰가 느껴질것입니다. Fast2test의 Microsoft AI-103덤프가 고객님의 시험패스테 조금이나마 도움이 되신다면 행복으로 느끼겠습니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
아직도 Microsoft인증AI-103시험준비를 어떻게 해야 할지 망설이고 계시나요? 고객님의 IT인증시험준비길에는 언제나 Fast2test가 곁을 지켜주고 있습니다. Fast2test시험공부자료를 선택하시면 자격증취득의 소원이 이루어집니다. Microsoft인증AI-103시험덤프는Fast2test가 최고의 선택입니다.
질문 # 43
You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?
정답：B
설명：
The Azure AI Content Safety image model uses a trimmed severity scale that returns only 0, 2, 4, and 6, so an odd value such as 3 cannot occur for image content. The text model uses the full 0 to 7 scale, which is where odd-numbered severities appear.
질문 # 44
You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?
정답：C
설명：
The Protected material text detection feature in Azure AI Content Safety is specifically designed to identify and block responses that may contain copyrighted or protected content, such as lyrics from popular songs. Since GPT-4 may have been trained on publicly available data, this feature helps ensure that the chatbot does not generate copyrighted lyrics by detecting and filtering out protected material.
질문 # 45
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?
정답：D
설명：
The best option in this scenario is a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing.
Custom Field Targeting with Confidence Scores: Azure Content Understanding allows you to build a document analyzer with a user-defined schema. By defining your required fields (InvoiceNumber, InvoiceDate, VendorName, and TotalAmount), the service will handle extraction across varying layouts and return a dedicated field-level confidence score. You can natively evaluate these confidence metrics to implement your supervisor routing workflow.
RAG-Ready Output Structure: The service naturally returns highly structured JSON payloads.
This makes it perfectly optimized to be ingested directly as grounding data for a Retrieval- Augmented Generation (RAG) agent.
Incorrect:
[Not C]
The prebuilt-layout analyzer extracts raw structural elements such as blocks of text, hierarchy, selection marks, and complete tables. It does not automatically classify or cleanly isolate specific target entities (like vendor name or total amount) into dedicated schema properties, leaving you with heavy post-processing work to isolate the text.
[Not D]
The prebuilt-documentSearch analyzer is optimized for broad, layout-aware text extraction for indexing. Relying on Azure AI Search's search.score for supervisor routing is conceptually flawed; search.score represents a relevance score for a search query ranking rather than an accuracy metric for data extraction confidence.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/analyzer-improvement
질문 # 46
Hotspot Question
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
- Authenticates by using a Microsoft Entra managed identity
- Sends prompts to a deployed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 47
You need to configure Agent1 to meet the security and compliance requirements.
What should you use?
정답：A
설명：
The correct answer is B. Personally Identifiable Information (PII) Detection . The case study states that Agent1 must never reveal customer information , even if a document containing customer data is added accidentally to the product sheet repository in storage1. This is a privacy and compliance control requirement, so the appropriate capability is PII Detection.
Azure Language PII Detection is a Foundry Tools capability that identifies, classifies, and redacts sensitive information across text, conversations, and native documents. Microsoft states that PII Detection can be used to implement privacy controls, reduce sensitive data exposure, and support compliance requirements. In this scenario, PII Detection should be applied to retrieved product-sheet content and generated responses so customer names, contact details, identifiers, and other sensitive values are not exposed to users.
Prompt Shields are important for a separate requirement: protecting Agent1 from malicious instructions hidden in documents or embedded text. Microsoft describes Prompt Shields for documents as protection against hidden instructions embedded in external content. However, the option that directly satisfies the requirement to prevent disclosure of customer information is PII Detection. Self-harm and violence filters address harmful-content categories, not privacy leakage.
질문 # 48
......
우리는 고객이 첫 번째 시도에서Microsoft AI-103 자격증시험을 합격할수있다는 것을 약속드립니다. Microsoft AI-103 시험을 합격하여 자격증을 손에 넣는다면 취직 혹은 연봉인상 혹은 승진이나 이직에 확실한 가산점이 될것입니다. Microsoft AI-103시험 어려운 시험이지만 저희Microsoft AI-103덤프로 조금이나마 쉽게 따봅시다.
AI-103유효한 인증공부자료: https://kr.fast2test.com/AI-103-premium-file.html