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| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language |
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NEW QUESTION # 77
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 78
An app that analyzes social media posts to identify their tone is an example of which type of natural language processing (NLP) workload?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure," sentiment analysis is an NLP workload that determines the emotional tone or opinion expressed in a piece of text. This could be positive, negative, or neutral sentiment.
When an app analyzes social media posts to identify their tone, it is performing sentiment analysis, since it aims to understand the emotional context behind user-generated text such as tweets, reviews, or comments.
Azure provides this functionality through the Azure Cognitive Services - Text Analytics API, which evaluates text and returns sentiment scores.
Other options are not suitable:
* Key phrase extraction identifies main ideas in text but not tone.
* Entity recognition identifies names of people, organizations, or locations.
* Speech recognition converts spoken words into text, not emotional analysis.
Therefore, analyzing social media tone is an example of sentiment analysis, a key NLP workload in Microsoft' s AI-900 syllabus.
NEW QUESTION # 79
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 80
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify guiding principles for responsible AI", responsible AI is built upon six foundational principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle serves to guide the ethical design, deployment, and management of artificial intelligence systems.
* Fairness - This principle ensures that AI systems treat all people fairly and do not discriminate based on personal attributes such as gender, race, or age. The Microsoft Learn content emphasizes that "AI systems should treat everyone fairly" and that organizations must evaluate datasets and model outputs for bias. In this scenario, "The system must not discriminate based on gender, race" clearly aligns with Fairness because it directly addresses equitable treatment and unbiased decision-making.
* Privacy and Security - Microsoft's responsible AI framework stresses that "AI systems must be secure and respect privacy." This means personal data should be safeguarded, processed lawfully, and visible only to authorized users. The statement "Personal data must be visible only to approved users" reflects the importance of protecting sensitive information and controlling access-precisely the intent of the Privacy and Security principle.
* Transparency - Transparency refers to ensuring that users understand how AI systems operate and make decisions. Microsoft notes that "AI systems should be understandable and users should be able to know why decisions are made." The requirement "Automated decision-making processes must be recorded so that approved users can identify why a decision was made" directly supports this principle.
Transparency promotes trust and accountability by documenting the reasoning behind AI outputs.
Reliability and Safety, though another core principle, does not directly relate to any of the provided statements in this question.
NEW QUESTION # 81
Which metric can you use to evaluate a classification model?
Answer: B
Explanation:
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
NEW QUESTION # 82
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