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| Section | Weight | Objectives |
|---|---|---|
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Identify common machine learning tasks - Describe core machine learning concepts - Describe Azure Machine Learning capabilities |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
>> AI-900 Real Exam Answers <<
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NEW QUESTION # 57
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
NEW QUESTION # 58
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
Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
NEW QUESTION # 59
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation
NEW QUESTION # 60
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 61
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 # 62
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