一つの試験だけでは多くの時間を無駄にする必要がありません。AI-103認定試験が大変難しいと感じて、多くの時間を取らなければならないとしたら、ツールとしてGoShikenのAI-103問題集を利用したほうがいいです。この問題集はあなたに時間を節約させることができますから。もっと重要なのは、この問題集はあなたが試験に合格することを保証できますから。この問題集よりもっと良いツールは何一つありません。試験の準備をするのにたくさんの時間を無駄にするより、そんな時間を利用してもっと有意義なことをしたほうがいいです。ですから、はやくGoShikenのサイトに行ってもっと多くの情報を読みましょう。この素晴らしきチャンスを逃したらきっと後悔しますよ。
| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
どんなに宣伝しても、あなたの自身体験は一番重要なことです。我々社のGoShikenからMicrosoft AI-103問題集デモを無料にダウンロードできます。多くの受験生は試験に合格できましたのを助けるMicrosoft AI-103ソフト版問題はあなたの大好きになります。AI-103問題集を使用してから、あんたはIT業界でのエリートになります。
質問 # 60
You are building an app that will use Azure AI to monitor workspaces for safety. You need to recommend a service that meets the following requirements:
* Generates alerts when employees enter high-risk areas
* Monitors video feeds in real time
* Minimizes development effort
What should you recommend?
正解：B
解説：
Azure Vision in Foundry Tools Spatial Analysis is designed to process real-time streaming video and analyze the presence, movement, and spatial relationships of people in physical environments. It provides predefined operations such as personcrossingpolygon, which can identify when a person enters or exits a configured zone. The resulting personZoneEnterExitEvent can be consumed by an application to trigger alerts when an employee enters an area designated as high risk.
Spatial Analysis minimizes development effort because it supplies pretrained people-detection and zone- monitoring capabilities. Developers configure camera streams, polygonal zones, and event parameters instead of collecting images, labeling objects, and training a custom model.
Image Analysis primarily evaluates individual images and does not maintain movement or zone state across a live stream. Azure AI Video Indexer extracts searchable insights from video or live-stream content, but it is not the specialized option for detecting physical zone-entry events. Custom Vision object detection would require training data, labeling, model training, deployment, and additional application logic to track movement.
The Study Guide explicitly associates Spatial Analysis with detecting the presence and movement of people in video under Implement computer vision solutions # Analyze videos .
質問 # 61
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line 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?
正解：D
解説：
The correct answer is C because the requirement is structured field extraction from invoices across varying templates, not only OCR or layout preservation. Azure Content Understanding analyzers are reusable configurations that combine content extraction, AI-powered analysis, and structured data output, and Microsoft states that custom analyzers can be created for specific extraction needs. In this case, the analyzer schema should define fields such as invoice number, invoice date, vendor name, and total amount so the output can be returned as structured JSON for downstream RAG grounding.
The confidence-routing requirement also points to Content Understanding field confidence scores. Microsoft documentation states that every field can include a confidence score from 0 to 1, and that confidence scores can be used to automate high-confidence results while routing low-confidence results for human review. A threshold such as 0.80 is therefore an application routing rule based on the returned field confidence. The prebuilt-layout analyzer preserves layout but does not define invoice-specific business fields. Groundedness guardrails evaluate generated answers, not invoice field extraction. Azure AI Search search.score measures retrieval relevance, not extraction confidence. Reference topics: Content Understanding custom analyzers, document field extraction, structured JSON output, confidence scoring, and RAG grounding.
質問 # 62
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?
正解：A
解説：
Serverless API deployment runs on Microsoft-managed infrastructure, bills per input and output token, and needs no VM quota, so it is the fastest path that matches token-based billing. It is the standard choice when you want pay-per-use economics without managing compute.
質問 # 63
A custom NER model uses one ContactInfo entity for phone numbers, email addresses, and social-media handles. Precision is low.
What should you do before retraining?
正解：A
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The existing ContactInfo entity combines several distinct entity patterns into one heterogeneous label. Phone numbers, email addresses, and social-media handles have substantially different syntax, contextual clues, and boundary patterns.
Splitting the broad entity into Phone , Email , and SocialMedia gives the custom NER model a clearer and more internally consistent training signal. Every existing tagged span should then be relabeled using the correct specialized entity class before retraining.
This can improve precision because the model no longer needs to learn one extremely broad decision boundary that covers unrelated forms of contact data. More precise labels also help the model learn the correct start and end boundaries of each entity, addressing the reported false positives in which adjacent text is included.
Adding more examples under the same ambiguous ContactInfo schema may reinforce the underlying modeling ambiguity. Auto-labeling reduces annotation effort but does not correct the schema problem.
Lowering a confidence threshold would generally cause more uncertain predictions to be accepted, tending to lower rather than improve precision.
Study Guide alignment: implement solutions that extract entities and customize language-model outputs for domain-specific extraction tasks . Microsoft Learn
質問 # 64
You configure the following system message:
"You are an AI assistant that helps people solve mathematical puzzles. Explain your answers as if the request is by a 4-year-old." Which prompt-engineering technique is this?
正解：A
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The system message is an example of priming because it establishes contextual expectations before the user supplies the actual mathematical problem.
The first sentence assigns the model a role: an assistant specialized in mathematical puzzles. The second establishes the expected communication style and audience level by requiring explanations suitable for a four- year-old. These instructions influence subsequent responses without modifying model weights.
It is not few-shot learning because no input/output examples are provided. Few-shot prompting would include several representative puzzles together with correctly formatted answers.
It is not chain-of-thought because the prompt does not explicitly provide or request a structured sequence of intermediate reasoning steps. An affordance is a cue that indicates a possible behavior or completion, but role and behavioral context established through a system message is most accurately described by the available option priming .
This technique is commonly used to establish persona, domain boundaries, tone, output style, and operating assumptions for a generative application.
The AI-103 Study Guide includes prompt engineering and adjustment of model behavior as part of optimizing generative AI systems. Microsoft Learn
質問 # 65
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