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The AI-900 certification exam is primarily designed for business stakeholders and technical professionals who want to gain a fundamental understanding of AI technologies and their potential applications in Microsoft Azure services. Microsoft Azure AI Fundamentals certification is an entry-level exam that validates the candidate's knowledge of AI concepts and Azure AI services, including Cognitive Services, Machine Learning, and Bot Services. AI-900 Exam also covers the ethical and responsible use of AI, including privacy, security, and compliance considerations.
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NEW QUESTION # 90
You have the following apps:
* App1: Understands the public perception of a brand or topic
* App2: Applies profanity filters to speech-to-text
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
App1: "Understands the public perception of a brand or topic" # Sentiment analysis According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn's Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.
For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.
App2: "Applies profanity filters to speech-to-text" # Language detection The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.
Other options such as Captioning or Named Entity Recognition (NER) are not relevant:
* Captioning describes images or videos, not speech filtering.
* NER identifies people, locations, or organizations but does not handle profanity or language detection.
Therefore, based on Azure AI NLP features:
* App1 uses Sentiment analysis
* App2 uses Language detection
NEW QUESTION # 91
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 92
You need to analyze images of vehicles on a highway and measure the distance between the vehicles. Which type of computer vision model should you use?
Answer: D
NEW QUESTION # 93
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
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.
NEW QUESTION # 94
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
"To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations"
NEW QUESTION # 95
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