ほとんどの人がAI-103ガイド急流を入手するのは容易ではありませんが、製品を選択する限り、資格AI-103証明書を簡単かつ効率的に取得できると思います。教材を選択したら、AI-103ガイドの質問から試験ポイントをマスターできます。その後、試験に合格するのに十分な自信があります。安全な環境と効果的な製品については、AI-103の質問トレントを試してみてください。
| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
テストの準備に多くの時間を費やし、それでも何度も失敗するのは馬鹿げていますか？ 一部の受験者は、Microsoft AI-103試験ダンプ問題で簡単に試験に合格しますか？ 試験に合格し、認定を取得することが目標である場合、AI-103試験ダンプは、目標を簡単に達成するのに役立ちます。選択してみませんか？ AI-103試験ダンプ問題を含むテストの前にわずか数十のお金と20〜35時間の有効な準備で、確実に試験をクリアできます。 では、なぜあなたは無駄な努力をするのに多くの時間を無駄にしているのですか？
質問 # 50
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 AI 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 image moderation to block unsafe content before processing the images.
Does this meet the goal?
正解：A
解説：
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
質問 # 51
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
正解：D
解説：
The correct answer is B. Add a reflection pass before the responses are returned . A reflection pass is an application-orchestration step in which the generated summary is reviewed before final delivery, typically by asking the model or an evaluator step to check whether the answer covers the retrieved policy evidence and to revise the response when important details are missing. This directly addresses response completeness in application logic before the response is returned. The Microsoft Learn study guide explicitly includes Implement model reflection and Apply prompt engineering techniques to improve responses under optimization and operationalization of generative AI solutions.
This is also consistent with Microsoft Foundry agentic-loop guidance, which identifies reflection and planning cycles as patterns for multi-step reasoning in production agent systems. Completeness is a response-quality property: Azure AI evaluation defines completeness as whether a response contains all necessary and relevant information with respect to ground truth.
Option C is not correct because the scenario already says the agent generates summaries from retrieved policy documents, which is already a grounded retrieval pattern. Option A mainly reduces randomness, not missing content. Option D improves delivery experience, not answer completeness. Reference topics: model reflection, prompt engineering, agentic loops, response evaluation, and grounded generative AI solutions.
質問 # 52
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
正解：
解説：
Explanation:
* Extract the text: Azure Vision in Foundry Tools
* Identify well-known locations: Azure Language in Foundry Tools
Azure Vision in Foundry Tools is the appropriate service for extracting text from road signs. Its pretrained Optical Character Recognition capability detects printed or handwritten text in images and returns recognized words, lines, and their coordinates. Microsoft specifically identifies street signs as an applicable OCR scenario. Because the OCR model is already trained, it avoids the data labeling and custom-model training that would increase development effort.
After OCR produces the text, Azure Language in Foundry Tools can analyze it through entity linking . Entity linking recognizes and disambiguates well-known entities and connects them to entries in a recognized knowledge base. This is suitable for determining that text on a sign represents a known geographic location rather than merely returning an unclassified text string. Named entity recognition can also classify extracted entities under categories such as Location, but entity linking provides the stronger interpretation implied by
"well-known locations."
Azure Document Intelligence is optimized for structured and text-heavy business documents, while Azure AI Search provides indexing and retrieval rather than primary OCR and linguistic analysis.
Study Guide alignment: implement OCR-based ingestion and use Foundry Tools to extract entities from text .
質問 # 53
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
正解：C
解説：
To enhance response completeness in your Microsoft Foundry agent, you must intercept the retrieved documents and the generated summary within your backend application logic before returning the payload to the user.
1. Implement Completeness Verification Logic
Add a verification step in your orchestration code (e.g., in your Python/Semantic Kernel or LangChain pipeline) that compares the generated summary against the retrieved chunks.' Map Key Assertions: Extract main policy rules from retrieved text.Cross-Reference Entities: Verify all key entities are in the summary.
Check Scope Coverage: Ensure every retrieved document is represented.
Scan for Gaps: Identify critical missing constraints or exceptions.
2. Apply Application-Level Mitigation Strategies
If the verification step detects that the summary is incomplete, use your code to correct it before the final response leaves your system.
Reference:
https://dev.to/moonrunnerkc/how-i-built-a-verification-layer-for-copilot-clis-multi-agent-output-4b7h
質問 # 54
You have a Python application That collects customer comments before posting them to a public forum.
You need to send a text comment to Azure Al Content Safety and return the self-harm severity from the response.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Create an AnalyzeTextOptions instance and set its required text property to the string stored in comment.
Microsoft ' s Python SDK reference defines text as a required string containing the text to analyze. Microsoft Learn Then submit the request through:
client.analyze_text(request)
The resulting AnalyzeTextResult exposes categories_analysis. The code can iterate through those results, locate the item whose category is TextCategory.SELF_HARM, and return its severity.
Azure AI Content Safety currently analyzes the primary categories Hate, SelfHarm, Sexual, and Violence and returns severity information that can be used by the application to enforce publishing policy. Microsoft Learn analyze_image is inappropriate because the input is textual. A nonstandard client_moderate_text method is not the current SDK operation. The request ' s categories field, when used, expects a list of category identifiers and not the comment itself.
The technically correct current Python pattern is therefore AnalyzeTextOptions(text=comment) followed by client.analyze_text(request).
Study Guide alignment: configure detection of safety issues and sensitive content in text-analysis solutions
. Microsoft Learn
質問 # 55
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