AI-900 無料問題集「Microsoft Azure AI Fundamentals」
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
正解：A
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解説: (JPNTest メンバーにのみ表示されます)
You are designing a system that will generate insurance quotes automatically.
Match the Microsoft responsible Al principles to the appropriate requirements.
To answer, drag the appropriate principle from the column on the left to its requirement on the right Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Match the Microsoft responsible Al principles to the appropriate requirements.
To answer, drag the appropriate principle from the column on the left to its requirement on the right Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正解：
You are authoring a Language Understanding (LUIS) application to support a music festival.
You want users to be able to ask questions about scheduled shows, such as: "Which act is playing on the main stage?" The question "Which act is playing on the main stage?" is an example of which type of element?
You want users to be able to ask questions about scheduled shows, such as: "Which act is playing on the main stage?" The question "Which act is playing on the main stage?" is an example of which type of element?
正解：D
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解説: (JPNTest メンバーにのみ表示されます)
Match the types of natural languages processing workloads 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.
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.
正解：
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
Match the Al solution to the appropriate task.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正解：
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.
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.
正解：
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/
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
正解：
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
正解：
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
正解：
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.
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.
正解：
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE; Each correct selection is worth one point.
NOTE; Each correct selection is worth one point.
正解：
