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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Topic 3: 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 |
| Topic 4: 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 |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
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NEW QUESTION # 197
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 198
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 199
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
NEW QUESTION # 200
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?
Answer: B
Explanation:
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 # 201
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://machinelearningmastery.com/difference-test-validation-datasets/
NEW QUESTION # 202
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