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Microsoft AI-900 Certification Exam covers a wide range of topics related to AI, including machine learning, natural language processing, computer vision, and conversational AI. It tests one's ability to understand the basic principles of AI and how to apply them to solve real-world problems using the Microsoft Azure platform. Additionally, it evaluates one's knowledge of the ethical and responsible use of AI technologies.
NEW QUESTION # 329
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
Answer: C
Explanation:
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 330
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:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION # 331
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?
Answer: D
Explanation:
According to the AI-900 study guide and Microsoft Learn module "Identify features of natural language processing workloads", Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. Tasks such as extracting key terms, summarizing documents, identifying topics, or determining sentiment fall under NLP workloads.
In this question, you have insurance claim reports stored as text, and you need to extract key terms to generate summaries. This matches the Text Analytics service in Azure Cognitive Services, which uses NLP techniques such as key phrase extraction to identify important concepts within textual data.
The other options are incorrect because:
* A. Conversational AI focuses on chatbots or dialogue systems.
* B. Anomaly detection identifies unusual data patterns, not textual meaning.
* D. Computer vision processes image or video content, not text.
Therefore, extracting key terms from documents is a clear example of Natural Language Processing.
Reference:Microsoft Learn - Identify features of Natural Language Processing (NLP) workloads
NEW QUESTION # 332
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:
NEW QUESTION # 333
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaluate-model
NEW QUESTION # 334
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