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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe responsible AI practices for generative AI |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision |
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NEW QUESTION # 226
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:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0
NEW QUESTION # 227
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.
Answer:
Explanation:
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
NEW QUESTION # 228
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 229
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 # 230
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
NEW QUESTION # 231
......
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