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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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問題 #67
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
答案:A
解題說明:
According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Azure Text Analytics service documentation, key phrase extraction is a natural language processing (NLP) technique used to automatically identify the main topics or talking points within a text document or a collection of documents. This feature is designed to summarize textual data by detecting the most relevant words or short phrases that capture the essence of the content.
For example, if a document discusses "renewable energy sources such as solar and wind power," the key phrases extracted might include "renewable energy," "solar power," and "wind power." This helps users quickly understand the primary focus areas of large volumes of text without manual review.
In Azure, the Text Analytics service provides several core NLP capabilities, including:
* Key phrase extraction - identifies main concepts or topics.
* Entity recognition - detects and categorizes proper names like people, locations, or organizations.
* Sentiment analysis - determines the emotional tone (positive, neutral, or negative).
* Language detection - identifies the language used in the text.
Since the question specifies identifying main talking points, the correct feature is key phrase extraction, as it focuses on summarizing themes rather than identifying entities or emotions.
Therefore, the verified answer is B. key phrase extraction.
問題 #68
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
答案:
解題說明:
問題 #69
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
答案:A
解題說明:
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview
問題 #70
To complete the sentence, select the appropriate option in the answer area.
答案:
解題說明:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
問題 #71
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?
答案:A
解題說明:
Explanation
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).
Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform over raw text.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
問題 #72
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