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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
| Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI |
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NEW QUESTION # 242
An app that analyzes social media posts to identify their tone is an example of which type of natural language processing (NLP) workload?
Answer: A
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 # 243
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and Microsoft Learn module "Describe principles of responsible AI", the transparency principle ensures that AI systems are understandable, explainable, and well-documented so that users, developers, and stakeholders can know how the system operates and makes decisions. Transparency involves clear communication, documentation, and interpretability.
Microsoft defines transparency as the responsibility to make sure that people understand how AI systems function, their limitations, and how decisions are made. For developers, this means providing detailed documentation and model interpretability tools so others can inspect, debug, and understand the AI model's behavior. For users, it means ensuring that the purpose, capabilities, and limitations of the AI system are clearly explained.
Providing documentation to help developers debug and understand how a service works directly aligns with this transparency principle. It ensures that the system's logic and behavior are open to inspection and that any unintended consequences can be identified and corrected. Transparency also builds trust in AI solutions by enabling accountability and oversight.
Let's analyze the other options:
* A. Ensure that all visuals have an associated text that can be read by a screen reader - This supports inclusiveness, not transparency, as it focuses on accessibility for all users.
* B. Enable autoscaling to ensure that a service scales based on demand - This is related to system performance and scalability, not responsible AI.
* D. Ensure that a training dataset is representative of the population - This supports fairness, as it prevents bias and ensures equitable outcomes.
Therefore, based on the official AI-900 training content and Microsoft's Responsible AI framework (which includes fairness, reliability, privacy, inclusiveness, transparency, and accountability), the correct answer is C.
Provide documentation to help developers debug code, because this directly promotes transparency in how the AI system operates and communicates its inner workings
NEW QUESTION # 244
You have a natural language processing (NIP) model that was created by using data obtained without permission.
Which Microsoft principle for responsible Al does this breach?
Answer: D
NEW QUESTION # 245
What is a form of unsupervised machine learning?
Answer: C
NEW QUESTION # 246
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 247
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