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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 2: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 3: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 4: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
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NEW QUESTION # 12
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure
NEW QUESTION # 13
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifie
NEW QUESTION # 14
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
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/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m
NEW QUESTION # 15
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In machine learning, especially within the Microsoft Azure AI Fundamentals (AI-900) framework, datasets used for supervised learning are composed of features (inputs) and labels (outputs). According to the Microsoft Learn module "Explore the machine learning process", a feature is any measurable property or attribute used by the model to make predictions, whereas a label is the actual value or category the model is trying to predict.
* Household Income # FeatureA feature (also known as an independent variable) represents the input data that the machine learning algorithm uses to detect patterns or correlations. In this dataset, Household Income is a numeric value that influences the prediction of house price categories. During training, the model learns how variations in household income correlate with changes in the house price category.
Microsoft Learn defines features as "the attributes or measurable inputs that are used to train the model." Thus, Household Income serves as a predictive input or feature.
* House Price Category # LabelThe label (or dependent variable) represents the output the model aims to predict. It is the known result during training that helps the algorithm learn correct mappings between features and outcomes. In this scenario, House Price Category-which can take values such as "Low,"
"Middle," or "High"-is the classification outcome that the model will predict based on household income (and possibly other variables). According to Microsoft Learn, "the label is the variable that contains the known values that the model is trained to predict." In summary, the dataset defines a supervised learning classification problem, where Household Income is the feature (input) and House Price Category is the label (output) that the model will learn to predict.
NEW QUESTION # 16
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:
Explanation
NEW QUESTION # 17
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