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Microsoft Designing and Implementing a Microsoft Azure AI Solution AI-102 Prüfungsfragen mit Lösungen (Q142-Q147):

142. Frage
You have an Azure Al Search resource named Search1.
You have an app named App1 that uses Search1 to index content.
You need to add a custom skill to App1 to ensure that the app can recognize and retrieve properties from invoices by using Search1.
What should you include in the solution?

Antwort: D

Begründung:
You're indexing content with Azure AI Search and you want to recognize and retrieve properties from invoices (such as invoice number, vendor, dates, totals, line items). The Azure service designed for structured information extraction from forms and documents is Azure AI Document Intelligence (formerly Form Recognizer).
In an Azure AI Search enrichment pipeline, you add a skillset. To pull structured fields from invoices, include a skill that invokes Document Intelligence's prebuilt Invoice model. This model already extracts common invoice fields, so you don't have to train anything-meeting the goal of minimal effort. You can wire this in using the built-in Document Intelligence skill for Search, or as a custom Web API skill that calls the Document Intelligence REST API, and then map the extracted fields into your index for retrieval and filtering.
Why the others are not suitable:
* Azure OpenAI is for generative/LLM tasks, not schema-level invoice field extraction.
* Azure AI Immersive Reader improves reading accessibility; it doesn't extract business fields.
* Azure Custom Vision is for image classification/object detection, not form/key-value extraction.
Microsoft References (Azure AI Solution)
* Azure AI Search skillsets - add AI enrichment and custom skills (Web API skill).
* Document Intelligence skill for Azure AI Search (formerly Form Recognizer skill) - integrate extraction into indexing.
* Prebuilt Invoice model - fields extracted (vendor, invoice ID/date, totals, taxes, line items, etc.).
* Azure AI Document Intelligence overview - key-value extraction and table/line-item parsing from documents.


143. Frage
You are developing a method for an application that uses the Translator API.
The method will receive the content of a webpage, and then translate the content into Greek (el). The result will also contain a transliteration that uses the Roman alphabet.
You need to create the URI for the call to the Translator API. You have the following URI.
https://api.cognitive.microsofttranslator.com/translate?api-version=3.0 Which three additional query parameters should you include in the URI? Each correct answer presents part of the solution. (Choose three.) NOTE: Each correct selection is worth one point.

Antwort: B,D,F

Begründung:
Comprehensive Detailed ExplanationYou are asked to construct a URI for the Microsoft Translator API call that:
* Translates webpage content into Greek (el).
* Returns a transliteration in the Roman alphabet (Latin script).
The base URI given is:
https://api.cognitive.microsofttranslator.com/translate?api-version=3.0 Now, let's evaluate the required query parameters.
* This specifies the target language for translation.
* Since the requirement is to translate into Greek, we must include:
* to=el
* Correct
1. to=el
* The source content is from a webpage.
* Translator API requires textType=html for web page or HTML-formatted content.
* If omitted, the default is plain text.
* Correct
2. textType=html
* By default, Greek text is written in its native Greek script.
* To provide a transliteration in the Roman alphabet, we must set toScript=Latn.
* Latn = Latin script.
* Correct
3. toScript=Latn
* A. toScript=Cyrl
* Cyrl = Cyrillic script. Not required here.
* B. from=el
* This would mean the source language is Greek.
* In the requirement, the target is Greek, so this is incorrect.
* E. textType=plain
* Incorrect, since we are translating HTML webpage content, not plain text.
Incorrect Options
The answer: C. textType=html, D. to=el, F. toScript=Latn
* Translator Text API v3.0 Reference - Translate
* Language and Script Parameters (to, from, toScript)
Microsoft References


144. Frage
You are building an app that will provide users with definitions of common AJ terms.
You create the following C# code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.

Antwort:

Begründung:

Explanation:

* High degree of certainty - No. The code uses default generation parameters for chat completions (e.g., default temperature # 1.0), which means responses are probabilistic and not guaranteed to be highly certain or consistent. Azure OpenAI guidance explains that parameters like temperature and top_p control randomness; to increase determinism/certainty you would lower temperature and/or adjust top_p, which this snippet does not do.
* Clarifying the user question - Yes. Prompt engineering guidance emphasizes that clear, specific instructions lead to better outputs. Rephrasing the user message from "What is an LLM?" to "What is an LLM in the context of AI models?" adds context and intent, improving the chance of getting the exact explanatory answer you want.
* Constrain with a stronger system message - Yes. In chat completions, the system message sets behavior and has the highest priority. Tightening it to "You must answer only within the context of AI language models." narrows scope and reduces topic drift, increasing the likelihood that the model's response stays on-task (definitions of AI terms, here specifically LLMs).
Microsoft References (Azure AI / Azure OpenAI Service)
* Azure OpenAI Service - Chat Completions: roles (system, user), message ordering, and usage.
* Azure OpenAI Service - Prompt engineering: write clear, specific prompts; add constraints to guide responses.
* Azure OpenAI Service - Sampling parameters: temperature and top_p control variability/creativity vs.
determinism.
* Azure OpenAI Service - Best practices for grounding, controllability, and reducing model drift with stronger system instructions.


145. Frage
Hotspot Question
You are building a message handling system that will use the Azure AI Translator service.
You need to ensure that incoming messages are translated to English.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:


146. Frage
You are building an app that will use Azure Al Language to extract meaning from text messages.
You need to provide additional context by adding references to supporting articles in Wikipedia.
What should you use?

Antwort: B

Begründung:
Entity linking in Azure AI Language identifies entities in text and links them to entries in a knowledge base such as Wikipedia, providing precisely the additional context and references you need. Custom NER extracts entities but doesn't link to Wikipedia; Content Safety moderates content; Key phrase extraction surfaces topics without external links.
Microsoft References:
* Entity linking overview (links entities to Wikipedia). Microsoft Learn
* How to call the Entity Linking API (returns entities with Wikipedia links)


147. Frage
......

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