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| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Identify common machine learning tasks - Describe core machine learning concepts - Describe features of no-code automated ML |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify Azure AI services for NLP - Identify common NLP tasks |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
>> AI-900 Ausbildungsressourcen <<
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261. Frage
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?
Antwort: D
Begründung:
Explanation
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
262. Frage
You need to make the press releases of your company available in a range of languages.
Which service should you use?
Antwort: B
Begründung:
Press release is a written communication. Speech wouldn't make sense. Plus, the Speech service doesn't translate languages, it "translates" audio into text, and vice versa.
https://docs.microsoft.com/en-us/learn/modules/translate-text-with-translation-service/2-get-started-azure
263. Frage
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
264. 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://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
265. Frage
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Antwort: B,D
Begründung:
According to the Microsoft Learn module "Explore speech capabilities of Azure AI" and the AI-900 Official Study Guide, speech synthesis (also known as text-to-speech) is the process of converting written text into spoken audio output. Azure's Speech service provides this functionality, allowing applications to produce human-like voices dynamically.
Let's evaluate each scenario:
* A. Automated voice that reads back a credit card number entered into a telephone keypad # YesThis is a classic text-to-speech (TTS) use case. The application converts numeric or textual input (such as a credit card number) into audio output that the caller hears. Azure Speech service can handle such voice responses in automated phone systems or IVR (Interactive Voice Response) setups.
* B. Generating live captions for a news broadcast # NoThis is a speech-to-text scenario (speech recognition), not speech synthesis. It involves converting audio speech into written text.
* C. Extracting key phrases from an audio recording of a meeting # NoThis involves speech-to-text followed by text analytics, not speech synthesis.
* D. An AI character in a computer game that speaks audibly to a player # YesThis is a direct example of speech synthesis, where the character's dialog text is converted into realistic spoken output for immersive interaction.
Therefore, based on Microsoft's AI-900 curriculum, speech synthesis is used in applications that convert text into audible speech, such as automated voice systems or interactive digital characters.
266. Frage
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