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Microsoft Azure AI Fundamentals Sample Questions (Q163-Q168):

NEW QUESTION # 163
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
* Include one or more faces.
* Contain at least one person wearing sunglasses.
What should you use to analyze the images?

A. the Describe Image operation in the Computer Vision service
B. the Analyze Image operation in the Computer Vision service
C. the Detect operation in the Face service
D. the Verify operation in the Face service

Answer: C

Explanation:
Section: Describe Artificial Intelligence workloads and considerations
Explanation/Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

NEW QUESTION # 164
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?

A. clustering
B. classification
C. regression

Answer: C

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 Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction

NEW QUESTION # 165
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

NEW QUESTION # 166
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.

Which type of computer vision was used?

A. object detection
B. semantic segmentation
C. image classification
D. optical character recognition (OCR)

Answer: A

Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

NEW QUESTION # 167
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 trip distance of individual taxi journeys
B. the number of taxi journeys in the dataset
C. the trip ID of individual taxi journeys
D. the fare of individual taxi journeys

Answer: A

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 # 168
......

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