AI-102試験番号、AI-102資格トレーニング

Drag to rearrange sections
HTML/Embedded Content

AI-102試験番号, AI-102資格トレーニング, AI-102日本語学習内容, AI-102受験内容, AI-102受験準備

P.S. GoShikenがGoogle Driveで共有している無料かつ新しいAI-102ダンプ：https://drive.google.com/open?id=1aJsm_B9XcdzQzxj2WwfE6cvmJEu_E4T9

GoShikenはその近道を提供し、君の多くの時間と労力も節約します。GoShikenはMicrosoftのAI-102認定試験に向けてもっともよい問題集を研究しています。もしほかのホームページに弊社みたいな問題集を見れば、あとでみ続けて、弊社の商品を盗作することとよくわかります。GoShikenが提供した資料は最も全面的で、しかも更新の最も速いです。

Microsoft AI-102試験は、AzureプラットフォームでAIソリューションを設計および実装する際の専門知識を実証するのに役立つ、やりがいのあるがやりがいのある認証です。キャリアを前進させたり、スキルを向上させたり、最新のテクノロジーを最新の状態に保ちたりする場合でも、この試験は競争力のあるAI雇用市場で能力を紹介し、目立つ能力を紹介する優れた方法です。

この試験は、自然言語処理、コンピュータビジョン、意思決定、音声認識など、AIに関連するさまざまなトピックをカバーしています。また、Azure Cognitive Services、Azure Bot Service、Azure Machine Learning、Azure DatabricksなどのAzure AIサービスを使用してソリューションを設計および実装する候補者の能力もテストします。

>> AI-102試験番号 <<

試験の準備方法-便利なAI-102試験番号試験-高品質なAI-102資格トレーニング

Microsoft　AI-102学習教材を選んだら、AI-102試験に落ちた人は少ないです。何故というと、AI-102学習教材の合格率が高いからです。AI-102学習教材は多くの人から好評をもらいました。そのほかに、AI-102学習教材は三種類があります。自分の好みによって選択できます。とても便利で、使い安いです。

Microsoft AI-102試験は、Microsoft Azureを使用してAIソリューションを設計および実装する専門家のスキルや知識を認定する認定試験です。この試験は、Azureプラットフォーム上でAIソリューションを設計および開発するための専門家である開発者、データサイエンティスト、およびAI専門家を対象としています。この試験に合格した候補者は、Microsoft Certified: Azure AI Engineer Associate認定を取得します。

Microsoft Designing and Implementing a Microsoft Azure AI Solution 認定 AI-102 試験問題 (Q178-Q183):

質問 # 178
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain sub-properties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Semantic Kernel
  • B. Liquid
  • C. JSONL
  • D. YAML
  • E. Handlebars

正解：B、E

解説：
Semantic Kernel supports multiple prompt template languages. Handlebars and Liquid both allow you to bind complex objects (with nested/sub-properties), iterate over collections, and use conditionals-ideal when your prompt needs structured data.
* JSONL is a dataset/record format, not a prompt templating language.
* YAML is a configuration/serialization format; while SK can store configs in YAML, YAML itself isn't a prompt templating engine for rendering nested objects in prompts.
* "Semantic Kernel" as an option is not a template language; SK provides the runtime and also a simple legacy template syntax, but for complex/nested objects Microsoft recommends Handlebars (cross- language) or Liquid (.NET).
References (Microsoft Docs):
* Handlebars prompt templates in Semantic Kernel (supports SK prompts, expressions, iteration, object binding). Microsoft Learn
* Liquid prompt templates in Semantic Kernel (example shows passing objects and iterating over nested data). Microsoft Learn
* SK prompt template syntax overview (context on SK templating options). Microsoft Learn


質問 # 179
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP request? To answer, drag the appropriate values to the correct targets. Each value 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:
Box 1: LargeFaceListID
LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.
Note: Given query face's faceId, to search the similar-looking faces from a faceId array, a face list or a large face list. A "faceListId" is created by FaceList - Create containing persistedFaceIds that will not expire. And a "largeFaceListId" is created by LargeFaceList - Create containing persistedFaceIds that will also not expire.
Incorrect Answers:
Not "faceListId": Add a face to a specified face list, up to 1,000 faces.
Box 2: matchFace
Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar


質問 # 180
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a chatbot that uses question answering in Azure Cognitive Service for Language Users report that the responses of the chatbot lack formality when answering spurious questions You need to ensure that the chatbot provides formal responses to spurious questions.
Solution: From Language Studio, you change the chitchat source to qna_chitchat_professional.tsv. and then retrain and republish the model.
Does this meet the goal?

  • A. Yes
  • B. No

正解：A

解説：
* The chatbot uses Question Answering in Azure Cognitive Service for Language.
* Users report that the chatbot's responses to spurious questions (such as jokes, casual chitchat, or off- topic queries) are not formal enough.
Comprehensive Detailed ExplanationIn Question Answering projects, you can add chitchat sources provided by Microsoft to handle small talk. These sources include:
* qna_chitchat_friendly.tsv # Informal/friendly responses.
* qna_chitchat_professional.tsv # Formal/professional responses.
* qna_chitchat_witty.tsv # Humorous/witty responses.
By changing the chitchat source to qna_chitchat_professional.tsv, then retraining and republishing, the bot will provide formal responses to spurious questions.
Therefore, this solution meets the goal.
The answer: A. Yes
* Add chit-chat to a Question Answering project
* Chit-chat personality types (friendly, professional, witty)
Microsoft References


質問 # 181
You are designing a solution that will answer questions about human resources (HR) policies stored in the PDF format.
You need to ensure that the identical answer to a specific question is returned every time. The solution must minimize development effort.
Which service should you include in the solution?

  • A. Azure Machine Learning
  • B. Azure OpenAI
  • C. Azure AI Document Intelligence
  • D. Azure AI Language

正解：B

解説：
You can create a Custom FAQs PDF Solution Powered by Azure OpenAI with Citations from Grounded Data.
Business Problem
Imagine an organization that frequently deals with FAQs, legal documents, or reports. They want an automated system that can:
Generate answers from questions using the information found in existing PDF documents.
Ensure citations from those documents are included in the responses.
Output a structured PDF with the Q&A and citations formatted in a professional and readable way.
Solution Overview
By leveraging Azure OpenAI on your data and Azure Cognitive Search, we can ground the OpenAI GPT model with custom documents like PDFs. When queried, the model retrieves the most relevant responses from the documents and includes citations such as document titles or page numbers and create a question-and-answer format JSON file. The system then generates a structured, professional-looking PDF with bold questions, clear answers, and citations.
Reference:
https://www.linkedin.com/pulse/creating-custom-faqs-pdf-solution-powered-azure-openai-saqlain- tahir-x9gse


質問 # 182
You have a web app that uses Azure AI search.
When reviewing activity, you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three action should you perform in sequence? To answer, more the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation:

Comprehensive Detailed Explanation
You are dealing with Azure AI Search (formerly Azure Cognitive Search). The issue is that the query key is compromised, and you need to ensure minimal downtime while keeping users with read-only access.
Azure AI Search uses two types of keys:
Admin keys (full control - manage indexes, data sources, etc.)
Query keys (read-only access for client applications)
If a query key is compromised, the remediation must be safe and fast, without breaking the application unnecessarily.
Step 1 - Add a new query key
Instead of immediately deleting the compromised key, first generate a new query key in the Azure portal or via API. This ensures you have a replacement ready before cutting off the old key.
Step 2 - Change the app to use the new key
Update your web app configuration (for example, connection strings or environment variables) so that it authenticates against Azure AI Search using the newly created query key. This step ensures a smooth transition with minimal downtime.
Step 3 - Delete the compromised key
Finally, once your app is verified to work with the new query key, remove the old compromised key to prevent any further unauthorized access.
Correct Order:
Add a new query key.
Change the app to use the new key.
Delete the compromised key.
Microsoft References
Manage admin and query keys in Azure AI Search
Best practices for key management


質問 # 183
......

AI-102資格トレーニング: https://www.goshiken.com/Microsoft/AI-102-mondaishu.html

P.S.GoShikenがGoogle Driveで共有している無料の2026 Microsoft AI-102ダンプ：https://drive.google.com/open?id=1aJsm_B9XcdzQzxj2WwfE6cvmJEu_E4T9

html    
Drag to rearrange sections
Rich Text Content
rich_text    

Page Comments