QUESTION: 29

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

classification
regression
clustering

Answer(s): B

Explanation:

In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

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QUESTION: 30

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?

the number of taxi journeys in the dataset
the trip distance of individual taxi journeys
the fare of individual taxi journeys
the trip ID of individual taxi journeys

Answer(s): B

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

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QUESTION: 31

You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?

classification
regression
clustering

Answer(s): B

Explanation:

In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

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QUESTION: 32

HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through
ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment.

Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.

Reference:

https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features

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Free AI-900 Exam Braindumps (page: 9)

QUESTION: 29

Explanation:

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QUESTION: 30

Explanation:

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QUESTION: 31

Explanation:

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QUESTION: 32

Explanation:

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