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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamental principles of machine learning on Azure15–20%- Describe capabilities of Azure Machine Learning
- Describe core concepts of machine learning
- Describe automated machine learning
- Describe machine learning pipelines
Topic 2: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
- Describe capabilities of Azure Translator
- Identify types of NLP solutions
Topic 3: Features of generative AI workloads on Azure20–25%- Describe generative AI concepts
- Describe use cases for generative AI
- Describe responsible AI practices for generative AI
- Describe capabilities of Azure OpenAI Service
Topic 4: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Face
- Describe capabilities of Azure Custom Vision
- Identify types of computer vision solutions
Topic 5: Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe considerations for developing AI solutions
- Describe responsible AI principles

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Microsoft Azure AI Fundamentals AI-900 Prüfungsfragen mit Lösungen (Q174-Q179):

174. Frage
Which Azure Cognitive Services service can be used to identify documents that contain sensitive information?

Antwort: B

Begründung:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common AI workloads," the Azure Form Recognizer service is part of Azure Cognitive Services for Document Intelligence. It enables organizations to extract, analyze, and identify information from structured and unstructured documents, including sensitive or confidential data such as names, addresses, financial figures, and identification numbers.
Form Recognizer uses optical character recognition (OCR) combined with machine learning to automatically extract key-value pairs, tables, and text fields from documents like invoices, receipts, contracts, and forms. It can be customized to identify and classify documents that contain specific sensitive data, allowing businesses to automate compliance and data governance tasks.
By contrast:
* A. Custom Vision is used for image classification and object detection - it analyzes visual data, not document content.
* B. Conversational Language Understanding (formerly LUIS) identifies intent and entities in text conversations, not document structure or sensitive data.
Form Recognizer is explicitly mentioned in the AI-900 course as the tool for document analysis and extraction. It can even integrate with Azure Cognitive Search or Azure Purview for further data management and compliance workflows.
Therefore, the verified and correct answer, aligned with Microsoft's official training content, is C. Form Recognizer, as it is the Azure Cognitive Service capable of identifying and processing documents containing sensitive information.


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

Antwort:

Begründung:

Explanation:

This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules "Explore natural language processing" and "Explore speech capabilities." These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.
* You can use the Speech service to transcribe a call to text # YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains:
"The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files." This makes it ideal for call transcription, voice assistants, and meeting captioning.
* You can use the Text Analytics service to extract key entities from a call transcript # YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: "Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data."
* You can use the Speech service to translate the audio of a call to a different language # YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language.
Microsoft Learn describes this as: "Speech Translation combines speech recognition and translation to translate spoken audio to another language."


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

Antwort:

Begründung:

Explanation:
Statement
Yes / No
Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.
Yes
A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.
Yes
An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.
No
This question is based on the Responsible AI principles defined by Microsoft, a major topic in the AI-900:
Microsoft Azure AI Fundamentals certification. The goal of Responsible AI is to ensure that artificial intelligence is developed and used ethically, safely, and transparently to benefit people and society. Microsoft' s framework defines six core principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability.
* Transparency Principle - YesProviding an explanation for a loan application decision clearly reflects transparency. According to Microsoft's Responsible AI guidelines, transparency involves ensuring that users and stakeholders understand how AI systems make decisions. When a financial AI model explains why a loan was approved or denied, it promotes user trust and confidence in automated decision- making. Transparency helps individuals understand influencing factors (like income or credit score), thereby fostering ethical AI deployment.
* Reliability and Safety Principle - YesA triage bot that prioritizes insurance claims based on injury severity demonstrates reliability and safety. This principle ensures that AI systems consistently operate as intended, handle data accurately, and do not cause unintended harm. For a triage bot, safety means it must correctly interpret medical or claim information and consistently provide appropriate prioritization. Microsoft emphasizes that reliable AI systems must be tested rigorously, function correctly in various scenarios, and maintain user safety at all times.
* Inclusiveness Principle - NoAn AI solution priced differently for various sales territories is unrelated to inclusiveness. Inclusiveness focuses on designing AI systems that are accessible and fair to all users, including those with disabilities or from different demographic backgrounds. Price variation across territories is a business strategy, not an ethical AI inclusion concern. Hence, this statement does not align with any Responsible AI principle.


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

Antwort:

Begründung:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml


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

Antwort:

Begründung:


179. Frage
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