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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
  • 1. Language modeling and translation
    • 2. Key phrase extraction
      • 3. Sentiment analysis
        Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
        • 1. Common AI workloads
          • 2. Responsible AI principles
            Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
            • 1. Training and validation concepts
              • 2. Supervised vs unsupervised learning
                Features of computer vision workloads on Azure15-20%- Computer vision solutions
                • 1. Image classification
                  • 2. OCR and image analysis
                    • 3. Object detection

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                      Microsoft AI-900題庫更新，AI-900測試題庫

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                      最新的 Microsoft Certified: Azure AI Fundamentals AI-900 免費考試真題 (Q283-Q288):

                      問題 #283
                      Select the answer that correctly completes the sentence.

                      答案：

                      解題說明：

                      Explanation:


                      問題 #284
                      To complete the sentence, select the appropriate option in the answer area.

                      答案：

                      解題說明：

                      Reference:
                      https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai


                      問題 #285
                      Select the answer that correctly completes the sentence.

                      答案：

                      解題說明：

                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.
                      In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.
                      Let's evaluate the other options:
                      * Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.
                      * Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.
                      * Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.
                      Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.
                      # Final answer: Object detection


                      問題 #286
                      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?

                      答案：C

                      解題說明：
                      https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
                      https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure


                      問題 #287
                      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：

                      Explanation:


                      問題 #288
                      ......

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