AI-901 Studienmaterialien: Microsoft Azure AI Fundamentals - AI-901 Torrent Prüfung & AI-901 wirkliche Prüfung

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Microsoft AI-901 Prüfungsplan:

Thema	Einzelheiten
Thema 1	Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Thema 2	Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Die Fragenkataloge von PrüfungFrage enthalten die Lernmaterialien und Simulationsfragen zur Microsoft AI-901 Zertifizierungsprüfung. Noch wichtiger bieten wir die originalen AI-901 Fragen Und Antworten.

Microsoft Azure AI Fundamentals AI-901 Prüfungsfragen mit Lösungen (Q124-Q129):

124. Frage
You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?

A. audio analyzer
B. image analyzer
C. video analyzer
D. document analyzer

Antwort: A

Begründung:
Voicemail recordings are audio content. Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video, and what elements to extract, including transcripts and structured fields.
Microsoft's custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.
Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer.

125. Frage
You have a dataset that contains experimental data for fuel samples.
You need to predict the amount of energy in kilojoules that can be obtained from a sample based on its measured density.
Which type of AI workload should you use?

A. Clustering
B. Classification
C. Regression
D. Knowledge mining

Antwort: C

Begründung:
Regression is a supervised machine learning technique which is used to predict continuous values. The ultimate goal of the regression algorithm is to plot a best-fit line or a curve between the data. The three main metrics that are used for evaluating the trained regression model are variance, bias and error.
Reference:
https://builtin.com/data-science/regression-machine-learning

126. Frage
What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. defines the tokens per minute (TPM) allocation for the model
B. defines constraints on the model's responses
C. selects which model to use
D. defines the agent's role and behavior
E. defines the Azure region where inference occurs

Antwort: B,D

127. Frage
Which Azure AI Foundry Service can be used to identify documents that contain sensitive information?

A. Azure AI Custom Vision
B. Azure AI Language
C. Azure AI Document Intelligence

Antwort: B

Begründung:
Azure AI Language can be used to identify documents containing sensitive information through its Personally Identifiable Information (PII) detection feature, which uses Named Entity Recognition (NER) to detect and categorize data like names, addresses, phone numbers, and financial details within unstructured text and native documents. This feature classifies sensitive data into predefined categories and can also redact the identified information, replacing it with asterisks to protect privacy.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable- information/overview

128. Frage
Hotspot Question
Select the answer that correctly completes the sentence.

Antwort:

Begründung:

Explanation:
Box 1: Named Entity Recognition (NER)
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text, along with other categories like names of people, organizations, and monetary values.
Reference:
https://www.iamdave.ai/blog/understanding-named-entity-recognition-ner-meaning-methods-and-use-cases/

129. Frage
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