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Microsoft AI-900 Exam

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Question 41
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
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Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

Question 42
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to access the web service? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. the model name
B. the training endpoint
C. the authentication key
D. the REST endpoint
You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
To consume the pipeline, you need:
- The REST endpoint for your service
- The Primary Key for your service
Reference:
https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service

Question 43
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
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Image AI-900_43R.png related to the Microsoft AI-900 Exam
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint.
Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

Question 44
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
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Image AI-900_44R.png related to the Microsoft AI-900 Exam
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.
Incorrect Answers:
- Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
- Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
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

Question 45
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
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Image AI-900_45R.png related to the Microsoft AI-900 Exam
Box 1: Yes -
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes -
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No -
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer


Question 46
HOTSPOT -
You have the following dataset.
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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.
Hot Area:
AI-900_46Q_2.png related to the Microsoft AI-900 Exam
Image AI-900_46R.png related to the Microsoft AI-900 Exam
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results

Question 47
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
AI-900_47Q.png related to the Microsoft AI-900 Exam
Image AI-900_47R.png related to the Microsoft AI-900 Exam
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

Question 48
Which AI service can you use to interpret the meaning of a user input such as "Call me back later?"
A. Translator Text
B. Text Analytics
C. Speech
D. Language Understanding (LUIS)
Text Analytics is an AI service that uncovers insights such as sentiment, entities, and key phrases in unstructured text.
Incorrect Answers:
D: Language Understanding (LUIS) is a cloud-based API service, not an AI service, that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis

Question 49
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
AI-900_49Q.png related to the Microsoft AI-900 Exam
Image AI-900_49R.png related to the Microsoft AI-900 Exam
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Question 50
You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?
A. Translator Text
B. QnA Maker
C. Speech
D. Language Understanding (LUIS)
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis



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