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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions |
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NEW QUESTION # 19
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________.
Answer:
Explanation:
Classification
Explanation:
NEW QUESTION # 20
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 21
Match the computer vision service to the appropriate Al workload.
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:
Explanation:
This question evaluates understanding of the different Azure AI Computer Vision services and their distinct functionalities, as covered in the Microsoft AI-900 study guide and Microsoft Learn modules under "Describe features of common AI workloads" and "Identify Azure services for computer vision."
* Azure AI Document Intelligence (formerly known as Form Recognizer):This service is designed to extract structured information from documents, such as forms, receipts, and invoices. It uses optical character recognition (OCR) combined with AI models to detect key-value pairs, tables, and handwritten text. This makes it ideal for automating data entry and digitizing scanned documents.
Hence, it matches "Extract information from scanned forms and invoices."
* Azure AI Vision (formerly Computer Vision):This service provides image and video analysis capabilities. It can detect objects, people, text, and scenes; generate image captions; and extract descriptive tags. It also supports OCR for printed and handwritten text within images. Therefore, it matches "Analyze images and video, and extract descriptions, tags, objects, and text."
* Azure AI Custom Vision:Custom Vision allows you to train your own image classification and object detection models using your own labeled images. Unlike the general Vision service, Custom Vision lets you build domain-specific models-for example, detecting your company's products or identifying manufacturing defects. Hence, it matches "Train custom image classification and object detection models by using your own images." These three services complement each other within Azure's computer vision ecosystem, collectively supporting both general-purpose and specialized AI solutions for visual data analysis.
NEW QUESTION # 22
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 # 23
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:
NEW QUESTION # 24
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