100% PASS QUIZ MICROSOFT - AI-900 PERFECT VALID LEARNING MATERIALS

100% Pass Quiz Microsoft - AI-900 Perfect Valid Learning Materials

100% Pass Quiz Microsoft - AI-900 Perfect Valid Learning Materials

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Tags: AI-900 Valid Learning Materials, Reliable AI-900 Exam Questions, AI-900 New Dumps Pdf, AI-900 Answers Free, Valid AI-900 Study Notes

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AI-900 Test Guide can guarantee that you can study these materials as soon as possible to avoid time waste. Microsoft Azure AI Fundamentals Study Question can help you optimize your learning method by simplifying obscure concepts. AI-900 Exam Questions will spare no effort to perfect after-sales services.

The Microsoft AI-900 Exam consists of 40-60 multiple-choice questions and has a time limit of 60 minutes. The passing score for the exam is 700 out of 1000. To prepare for the exam, Microsoft offers a variety of resources, including online training courses, practice exams, and study guides.

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We all know that it is not easy to prepare the AI-900 exam; there are thousands of candidates to compete with you. So it is a fierce competition. If you want to win out in the exam, you need the professional study materials to guide you. Our AI-900 Study Materials are confident to ensure that you will acquire the certificate. And the pass rate of our AI-900 practice guide is high to 98% to 100%.

The AI-900 exam covers a range of topics, including AI workloads and considerations, fundamental principles of machine learning, and common machine learning scenarios and algorithms. AI-900 exam also covers Azure AI services and tools such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. Passing AI-900 Exam demonstrates that an individual has a good understanding of AI and its applications in Azure and is capable of designing and implementing AI solutions using Azure services.

Microsoft Azure AI Fundamentals Sample Questions (Q86-Q91):

NEW QUESTION # 86
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?

  • A. fairness
  • B. inclusiveness
  • C. reliability and safety
  • D. accountability

Answer: B


NEW QUESTION # 87
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?

  • A. object detection
  • B. semantic segmentation
  • C. image classification
  • D. optical character recognition (OCR)

Answer: D

Explanation:
Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text


NEW QUESTION # 88
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features


NEW QUESTION # 89
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 # 90
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.

Which type of computer vision was used?

  • A. object detection
  • B. semantic segmentation
  • C. optical character recognition (OCR)
  • D. image classification

Answer: A

Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. 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. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection


NEW QUESTION # 91
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