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NEW QUESTION: 1
A. Option B
B. Option A
C. Option D
D. Option C
E. Option E
Answer: C,E
NEW QUESTION: 2
Which three fields are available when creating a service request? (Choose three.)
A. Summary
B. Storeroom
C. Asset
D. Affected Location
E. Company
F. Details
Answer: A,C,F
NEW QUESTION: 3
Who are typical target buyer(s) for Trusteer?
A. Line of Business and IT Management
B. Chief Marketing Officer (CMO)
C. Chief Information Officer (CIO)
D. Line of Business and Chief Information Security Officer (CISO)
Answer: D
NEW QUESTION: 4
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 fare of individual taxi journeys
B. the trip distance of individual taxi journeys
C. the trip ID of individual taxi journeys
D. the number of taxi journeys in the dataset
Answer: B
Explanation:
Explanation
The label is the column you want to predict. The identified Features are 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