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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
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NEW QUESTION # 54
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?
Answer: D
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 # 55
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point
Answer:
Explanation:
Explanation
Graphical user interface, application Description automatically generated.
NEW QUESTION # 56
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector
NEW QUESTION # 57
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:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 58
You need to predict the animal population of an area.
Which Azure Machine Learning type should you use?
Answer: C
Explanation:
According to the AI-900 official study materials, regression is a type of supervised machine learning used to predict continuous numeric values. Predicting the animal population of an area involves estimating a numeric quantity, which makes regression the appropriate model type.
Microsoft Learn defines regression workloads as predicting real-valued outputs, such as:
* Forecasting sales or demand.
* Predicting housing prices.
* Estimating resource usage or population sizes.
In contrast:
* Classification predicts discrete categories (e.g., "cat" or "dog").
* Clustering groups data into similar clusters but doesn't produce numeric predictions.
Therefore, because the task requires predicting a numerical population size, the verified answer is C.
Regression, as per Microsoft's AI-900 official guidelines.
NEW QUESTION # 59
......
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