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Explain Computer Vision Workloads Features on Azure (15-20%)

Here, the following skills will be measured:

• Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
• Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.

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Microsoft Azure AI Fundamentals Sample Questions (Q162-Q167):

NEW QUESTION # 162
To complete the sentence, select the appropriate option in the answer area.
Answer: ** Explanation:
Explanation
Graphical user interface, table Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 163**
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/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
NEW QUESTION # 164**
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 # 165**
What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. area under curve (AUC)
• B. coefficient of determination (R2)
• C. root mean squared error (RMSE)
• D. balanced accuracy
• E. F1 score

Answer: B,C Explanation:
Explanation
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
NEW QUESTION # 166
Which AI service can you use to interpret the meaning of a user input such as "Call me back later?"

• A. Text Analytics
• B. Language Understanding (LUIS)
• C. Translator Text
• D. Speech

Answer: A Explanation:
Explanation
Text Analytics is an AI service that uncovers insights such as sentiment, entities, and key phrases in unstructured text.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
NEW QUESTION # 167
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