[Oct 30, 2024] AI-900 Exam Dumps - Microsoft Practice Test Questions [Q66-Q84]

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[Oct 30, 2024] AI-900 Exam Dumps - Microsoft Practice Test Questions

New Real AI-900 Exam Dumps Questions


Microsoft AI-900 certification exam, also known as the Microsoft Azure AI Fundamentals exam, is a fundamental-level exam that measures a candidate's knowledge and understanding of essential concepts related to artificial intelligence (AI) and machine learning (ML) on the Azure platform. Microsoft Azure AI Fundamentals certification exam is intended for individuals who wish to demonstrate their knowledge of AI and ML concepts and their practical applications using the Azure platform. AI-900 exam is designed to validate that the candidate has the foundational knowledge required to use Azure AI services to build intelligent solutions.

 

NEW QUESTION # 66
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:

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


NEW QUESTION # 67
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
Graphical user interface, text, application, email Description automatically generated


NEW QUESTION # 68
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
Graphical user interface, text, application, email Description automatically generated


NEW QUESTION # 69
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

  • A. clustering
  • B. regression
  • C. classification

Answer: A

Explanation:
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/studio-module-reference/machine-learning-initialize- model-clustering


NEW QUESTION # 70
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

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector


NEW QUESTION # 71
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 trip distance of individual taxi journeys
  • C. the trip ID of individual taxi journeys
  • D. the fare of individual taxi journeys

Answer: B

Explanation:
The label is the column you want to predict. The identified Features are 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 # 72
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

  • A. Form Recognizer
  • B. Text Analytics
  • C. Custom Vision
  • D. Ink Recognizer

Answer: A

Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
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/


NEW QUESTION # 73
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features


NEW QUESTION # 74
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 # 75
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/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0 All 3 are correct as they are the different channels to connect with a bot Office 365 email - Enable a bot to communicate with users via Office 365 email.
Microsoft Teams - Configure a bot to communicate with users through Microsoft Teams.
Web Chat - Automatically configured for you when you create a bot with the Bot Framework Service.
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0


NEW QUESTION # 76
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://machinelearningmastery.com/difference-test-validation-datasets/


NEW QUESTION # 77
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 78
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks


NEW QUESTION # 79
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/machine-learning/studio-module-reference/anomaly-detection


NEW QUESTION # 80
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:
Explanation

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features


NEW QUESTION # 81
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaluate-model


NEW QUESTION # 82
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 83
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:
Graphical user interface, text, application, chat or text message Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines


NEW QUESTION # 84
......


Microsoft AI-900 exam is divided into various sections that cover different aspects of AI and Azure. These sections include understanding AI workloads and considerations, understanding fundamental principles of machine learning on Azure, and exploring various AI services and their applications on Azure.


Microsoft AI-900 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe Artificial Intelligence workloads and considerations: It identifies specifications of common AI workloads and guiding principles for responsible AI.
Topic 2
  • Describe fundamental principles of machine learning on Azure: It describes core machine learning concepts and Azure Machine Learning capabilities. Moreover, the topic also delves into identifying common machine learning techniques.
Topic 3
  • Describe features of Natural Language Processing (NLP) workloads on Azure: In this topic, questions about common NLP Workload Scenarios features, Azure tools, and services for NLP workloads appear.
Topic 4
  • Describe features of computer vision workloads on Azure: This topic discusses identifying common types of computer vision solution, Azure tools and services for computer vision tasks.
Topic 5
  • Describe features of generative AI workloads on Azure: Features of generative AI solutions and capabilities of Azure OpenAI Service are discussed in this topic.

 

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