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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
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NEW QUESTION # 68
Match the Azure Cognitive Services to the appropriate Al workloads.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 69
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://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
NEW QUESTION # 70
Select the answer that correctly completes the sentence
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of Computer Vision workloads on Azure", Object Detection is a specific computer vision capability used to identify and locate multiple types of objects within a single image. Unlike image classification, which assigns one label to an entire image, object detection identifies individual objects, their categories, and their positions using bounding boxes or polygons.
In practical terms, Object Detection combines two key outputs:
* Classification - recognizing what the object is (for example, "car", "person", "dog").
* Localization - determining where the object appears in the image by drawing bounding boxes around it.
This technology is commonly used in scenarios such as traffic monitoring (detecting vehicles and pedestrians), retail shelf analysis (detecting products and inventory levels), and manufacturing quality control (identifying defective parts).
Microsoft's Azure Cognitive Services - Custom Vision includes a dedicated Object Detection domain, which allows developers to train custom models to recognize multiple object types within a single image. The service uses deep learning techniques, particularly convolutional neural networks (CNNs), to process pixel patterns and spatial relationships for accurate detection.
For contrast:
* Image Classification identifies only the overall category of an image (e.g., "This is a cat").
* Image Description generates captions summarizing the visual content (e.g., "A cat sitting on a couch").
* Optical Character Recognition (OCR) detects and extracts text from images, not physical objects.
Therefore, per the official AI-900 learning content and Azure documentation, when the goal is to identify multiple types of items within a single image, the correct AI workload is Object Detection.
NEW QUESTION # 71
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/dotnet/machine-learning/resources/tasks
NEW QUESTION # 72
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 73
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