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Microsoft AI-900 - Microsoft Azure AI Fundamentals

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Total 319 questions

Match the types of AI 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.

Which metric can you use to evaluate a classification model?

A.

true positive rate

B.

mean absolute error (MAE)

C.

coefficient of determination (R2)

D.

root mean squared error (RMSE)

To complete the sentence, select the appropriate option in the answer area.

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.

coefficient of determination (R2)

B.

F1 score

C.

root mean squared error (RMSE)

D.

area under curve (AUC)

E.

balanced accuracy

You need to predict the sea level in meters for the next 10 years.

Which type of machine learning should you use?

A.

classification

B.

regression

C.

clustering

You need to create a training dataset and validation dataset from an existing dataset.

Which module in the Azure Machine Learning designer should you use?

A.

Select Columns in Dataset

B.

Add Rows

C.

Split Data

D.

Join Data

You use Azure Machine Learning designer to publish an inference pipeline.

Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

A.

the model name

B.

the training endpoint

C.

the authentication key

D.

the REST endpoint

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

To complete the sentence, select the appropriate option in the answer area.

To complete the sentence, select the appropriate option in the answer area.