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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Translator |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
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NEW QUESTION # 284
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 285
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/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
NEW QUESTION # 286
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Graphical user interface, text, application Description automatically generated
Box 1: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION # 287
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/cognitive-services/Translator/translator-info-overview
https://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note
NEW QUESTION # 288
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
NEW QUESTION # 289
......
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