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NEW QUESTION 25

You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do?

A. Register the training data.
B. Explain the model.
C. Provision an inference duster.
D. Register the model.

Answer: D

NEW QUESTION 26

You have a dataset that contains information about taxi journeys that occurred during a given period.

You need to train a model to predict the fare of a taxi journey.

What should you use as a feature?

A. the number of taxi journeys in the dataset
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys

Answer: B

Explanation:

Section: Describe fundamental principles of machine learning on Azure

Explanation/Reference:

Explanation:

The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label.

Example:

The provided data set contains the following columns:

vendor_id: The ID of the taxi vendor is a feature.

rate_code: The rate type of the taxi trip is a feature.

passenger_count: The number of passengers on the trip is a feature.

trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.

trip_distance: The distance of the trip is a feature.

payment_type: The payment method (cash or credit card) is a feature.

fare_amount: The total taxi fare paid is the label.

Reference:

https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

NEW QUESTION 27

You plan to apply Text Analytics API features to a technical support ticketing system.

Match the Text Analytics API features to the appropriate natural language processing scenarios.

To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

NEW QUESTION 28

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:

Explanation

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector

NEW QUESTION 29

You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?