多くの人は、MicrosoftインターネットでAI-103学習準備を購入するとプライバシーが明らかになることを心配することがよくあります。 一部の人々は、一部のWebサイトDeveloping AI Apps and Agents on Azureで製品を購入した後、匿名のSMS広告やテレマーケティングに悩まされることがよくあります。 しかし、プラットフォームでAI-103テスト資料を購入すると、このような状況Developing AI Apps and Agents on Azureは決して起こりません。 ここでは、顧客のプライバシーと購入情報をしっかりと保護し、顧客情報の開示は行わないことを厳soleに約束します。 AI-103準備トレントをAI-103購入すると、購入情報を入力するCertShiken専任の営業担当者がいます。 取引終了後、すべての顧客情報を保持および破棄する専門スタッフもいます。
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 2: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 3: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Topic 4: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 5: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
MicrosoftのAI-103試験に合格するのに、私たちは最も早い時間で合格するのを追求します。私たちはお客様のための利益を求めるのを追求します。私たちはCertShikenです。CertShikenはMicrosoftのAI-103問題集の正確性と高いカバー率を保証します。MicrosoftのAI-103問題集を購入したら、CertShikenは一年間で無料更新サービスを提供することができます。は
質問 # 48
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool may be
used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Access up-to-date information from public websites: Grounding with Bing Search Perform calculations during conversations: Code interpreter Retrieve information from documents uploaded directly to the agent: File search The correct tool for public, current web information is Grounding with Bing Search . Microsoft Foundry Agent Service identifies Grounding with Bing Search as the built-in tool that enables an agent to access and return information from the internet, which fits the requirement for up-to-date public website data. ( learn.
microsoft.com )
For calculations during conversations, use Code interpreter . Microsoft's Foundry guidance states that Code Interpreter enables an agent to run Python code in a sandboxed execution environment and solve data analysis and math tasks iteratively. This is the correct fit for financial analysts who need calculations, analysis, and potentially chart generation during the conversation.
For documents uploaded directly to the agent, use File search . Microsoft describes File Search as the tool that enables Foundry agents to search through documents, retrieve relevant information, and augment model responses with knowledge from uploaded files such as PDFs, Word documents, and proprietary content.
Computer use is for interacting with graphical applications, not calculation or document retrieval. Microsoft Fabric is for enterprise data and analytics integration, not direct uploaded document retrieval. Reference topics: Foundry Agent Service tools, Code Interpreter, File Search, and Grounding with Bing Search.
質問 # 49
You have a Python application that uses Azure OpenAt structured outputs to extract fields from unstructured receipt text and support ticket text. The application uses the following schema.
正解：
解説：
Explanation:
* The schema requires that a receipt has a discount_code value - No
* The schema can be used as is for Azure OpenAI structured outputs - No
* The generated JSON object will preserve the property order merchant, order_number, discount_code, line_items, and total - Yes The discount_code definition permits a nullable result. Consequently, the output can contain " discount_code
" : null when the source receipt contains no discount code. The property can be structurally present without containing a substantive discount-code value; therefore, the first statement is No .
The schema cannot be submitted unchanged. Azure OpenAI structured outputs support only a defined subset of JSON Schema. Every property declared in an object must be included in that object's required array.
Optional information must instead be represented as a required property whose type includes null. Each object must also specify " additionalProperties " : false. Furthermore, although nested anyOf constructs are supported, an anyOf construct cannot be used as the root schema. A schema that violates any of these restrictions must be revised before use, making the second statement No .
The third statement is Yes . Azure OpenAI structured outputs preserve keys in the order in which the properties are defined in the supplied schema. Therefore, the receipt object will emit merchant, order_number, discount_code, line_items, and total in that sequence. This behavior supports predictable parsing and downstream integration. The relevant curriculum area is Use Azure OpenAI in Foundry Models to generate content , including application integration and structured model responses.
質問 # 50
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
正解：
解説：
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
質問 # 51
You need to optimize content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Safety metaprompt feature to run the tests.
Does this meet the requirement?
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 Safety metaprompt is not the Content Safety feature intended to measure harm-category severity and tune moderation thresholds against representative sample text.
A safety metaprompt provides additional behavioral instructions intended to guide a generative model toward safer responses. It can complement other safety controls, but it does not perform the specific text moderation test required to determine whether configured severity thresholds accept or reject sample prompts.
For threshold testing, Microsoft provides the Moderate text content experience. The user supplies sample text, selects Run test , and Content Safety returns detected categories and severity values. The Configure filters interface can then be used to change allowed or prohibited severity levels and retest the samples.
Microsoft documents severity classifications including safe, low, medium, and high. Microsoft Learn Therefore, the proposed Safety metaprompt solution does not meet the stated requirement.
This maps to the AI-103 objectives to configure safety filters, guardrails, risk detection, and content moderation , and to apply responsible-AI safety evaluations. Microsoft Learn
質問 # 52
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Prompt shields action: Set action to block.
Additional mitigation: Enable Spotlighting.
The correct configuration is to set the Prompt Shields document attack action to block and enable Spotlighting
. Prompt Shields in Microsoft Foundry are designed to detect attempts to manipulate model behavior through adversarial input. Microsoft distinguishes document attacks from direct user prompt attacks: document attacks are malicious instructions embedded in third-party content such as documents, webpages, emails, or other externally supplied material. In this scenario, the embedded text inside uploaded screenshots is third-party content and can attempt to override the agent's instructions. Setting the action to block prevents detected document-attack content from being processed normally, which is required because the goal is prevention rather than passive logging or annotation.
Spotlighting is the additional mitigation because it marks or transforms document content so the model treats it as lower trust than system and user instructions. Microsoft's Foundry guidance describes Spotlighting as a Prompt Shields subfeature that helps protect against indirect or embedded document attacks by tagging input documents with special formatting to indicate lower trust. A custom blocklist is insufficient for unknown attacks, and OCR alone only extracts the malicious text; it does not mitigate prompt injection. Reference topics: Prompt Shields, document attacks, guardrails, Spotlighting, multimodal safety, and prompt injection defense.
質問 # 53
......
あなたはAI-103試験資料をよく勉強する限り、きっと短い時間で有難いAI-103認定試験資格証明書を取得できます。その後、あなたの生活もきっと大きく変わります。そして、いい友達ができ、いい生活を送ります。躊躇しないで、未来は本当に美しいです！AI-103試験資料有効であるかどうか分からない場合、Microsoftウエブサイトで、AI-103試験資料のデモを無料でダウンロードしてください。
AI-103日本語サンプル: https://www.certshiken.com/AI-103-shiken.html