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Microsoft AI-900, also known as the Microsoft Azure AI Fundamentals exam, is a certification exam designed to test one's knowledge and understanding of the fundamentals of artificial intelligence (AI) and its application on the Microsoft Azure platform. Microsoft Azure AI Fundamentals certification exam is ideal for individuals who are interested in pursuing a career in AI or who want to demonstrate their expertise in AI to potential employers.
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Microsoft AI-900 (Microsoft Azure AI Fundamentals) Certification Exam is designed to validate the foundational understanding of AI and its applications in Azure. It is an excellent entry-level certification for individuals who want to develop their knowledge of AI and how it can be leveraged in Azure. AI-900 Exam is suitable for anyone interested in AI, including business decision-makers, technical salespeople, developers, and IT professionals.
NEW QUESTION # 84
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:
Graphical user interface, text, application Description automatically generated
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 # 85
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - Ingest and prepare a dataset.
2 - Split the data randomly into training data....
3 - Tarin the model.
4 - Evaluate the model against the validation dataset.
NEW QUESTION # 86
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task 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
Diagram Description automatically generated
Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
* Which companies were mentioned in a news article?
* Does a tweet contain the name of a person? Does the tweet also provide his current location?
* Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentimen
NEW QUESTION # 87
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION # 88
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 # 89
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