AI-900 Exam Questions, AI-900 study materials. Microsoft Azure AI Fundamentals
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Microsoft Azure AI Fundamentals Sample Questions (Q244-Q249):
NEW QUESTION # 244
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, email Description automatically generated
NEW QUESTION # 245
What is a use case for classification?
- A. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
- B. predicting how many minutes it will take someone to run a race based on past race times
- C. analyzing the contents of images and grouping images that have similar colors
- D. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
Answer: C
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
NEW QUESTION # 246
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 247
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. optical character recognition (OCR)
- C. image classification
- D. semantic segmentation
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 # 248
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 249
......
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