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Get well prepared with the latest and actual questions of the Microsoft Azure AI Fundamentals exam.
Download Microsoft Azure AI Fundamentals Exam Dumps
NEW QUESTION 25
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
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 fare of individual taxi journeys
- C. the trip distance of individual taxi journeys
- D. the trip ID of individual taxi journeys
Answer: C
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:
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 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 fare of individual taxi journeys
- C. the trip distance of individual taxi journeys
- D. the trip ID of individual taxi journeys
Answer: C
Explanation:
Section: Describe fundamental principles of machine learning on Azure
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 28
What is a use case for classification?
- A. analyzing the contents of images and grouping images that have similar colors
- B. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
- C. predicting how many minutes it will take someone to run a race based on past race times
- D. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
Answer: A
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
NEW QUESTION 29
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