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Microsoft AI-900 Certification Exam is an excellent starting point for individuals who want to develop their Azure AI skills. By passing the exam, you will demonstrate your knowledge of AI and ML concepts, and you will have a better understanding of how these technologies can be used to solve real-world problems. You will also learn how to use Azure AI services and tools to build intelligent applications that can analyze data, recognize speech, and understand natural language.

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Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.
Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.

Microsoft Azure AI Fundamentals Sample Questions (Q23-Q28):

NEW QUESTION # 23
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation:
With Microsoft's Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more.
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect- and-manage-intelligent-bots

NEW QUESTION # 24
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. image classification
B. object detection
C. optical character recognition (OCR)
D. semantic segmentation

Answer: B

Explanation:
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 # 25
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 26
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

Box 1: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Box 2: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters.
Box 3: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/

NEW QUESTION # 27
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 28
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