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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
| Topic 2: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities |
| Topic 3: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 5: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Describe features of no-code automated ML |
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NEW QUESTION # 137
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:
This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.
NEW QUESTION # 138
You are building a tool that will process images from retail stores and identity the products of competitors.
The solution must be trained on images provided by your company.
Which Azure Al service should you use?
Answer: A
NEW QUESTION # 139
You have the process shown in the following exhibit.
Which type AI solution is shown in the diagram?
Answer: B
NEW QUESTION # 140
Select the answer that correctly completes the sentence
Answer:
Explanation:
Explanation:
NEW QUESTION # 141
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals official study guide and Microsoft Learn module
"Describe features of conversational AI workloads on Azure", Azure Bot Service is the core Azure platform for building, testing, deploying, and managing conversational agents or chatbots. These bots can communicate with users across multiple channels, including email, Microsoft Teams, Slack, Facebook Messenger, and webchat.
Azure Bot Service integrates deeply with the Bot Framework SDK and Azure Cognitive Services such as Language Understanding (LUIS) or Azure AI Language, enabling natural language processing and multi- channel message delivery. The service abstracts away channel management, meaning that developers can build one bot logic that connects seamlessly to several communication platforms.
Option analysis:
* A. Text Analytics is a Cognitive Service used for text mining tasks like key phrase extraction, language detection, and sentiment analysis - not for building chatbots.
* C. Translator provides language translation but cannot manage conversations or multi-channel delivery.
* D. Form Recognizer extracts structured information from documents and forms - unrelated to conversational interaction.
The AI-900 course explicitly defines Azure Bot Service as "a managed platform that enables intelligent, multi- channel conversational experiences between users and bots." This service allows businesses to unify chat experiences across multiple digital communication channels.
Thus, based on the official Microsoft Learn content and AI-900 syllabus, the best and verified answer is B.
Azure Bot Service, as it is the designated Azure solution for deploying a single conversational AI experience accessible from multiple platforms such as email, Teams, and webchat.
NEW QUESTION # 142
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