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The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.
Microsoft AI-900 (Microsoft Azure AI Fundamentals) certification exam is designed to provide a foundational understanding of artificial intelligence and its applications in the Azure platform. AI-900 Exam is suitable for individuals who are new to AI and want to gain knowledge of its basic concepts and how it can be leveraged in the cloud. By earning this certification, candidates will demonstrate their ability to identify common AI workloads and the tools available in Azure to build intelligent solutions.
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The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.
NEW QUESTION # 334
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." These materials explain that facial recognition tasks can be categorized into four major operations: verification, identification, similarity, and grouping. Each task serves a distinct purpose in facial recognition scenarios.
* Verification - "Do two images of a face belong to the same person?"The verification task determines whether two facial images represent the same individual. Azure Face API compares the facial features and returns a confidence score indicating the likelihood that the two faces belong to the same person.
* Similarity - "Does this person look like other people?"The similarity task compares a face against a collection of faces to find visually similar individuals. It does not confirm identity but measures how closely two or more faces resemble each other.
* Grouping - "Do all the faces belong together?"Grouping organizes a set of unknown faces into clusters based on similar facial features. This is used when identities are not known beforehand, helping discover potential duplicates or visually similar clusters within an image dataset.
* Identification - "Who is this person in this group of people?"The identification task is used when the system tries to determine who a specific person is by comparing their face against a known collection (face database or gallery). It returns the identity that best matches the input face.
According to Microsoft's AI-900 training, these tasks form the basis of Azure Face API's capabilities. Each helps solve a different type of facial recognition problem-from matching pairs to discovering unknown identities-making them essential components of responsible AI-based vision systems.
NEW QUESTION # 335
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
NEW QUESTION # 336
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
Include one or more faces.
Contain at least one person wearing sunglasses.
What should you use to analyze the images?
Answer: C
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
NEW QUESTION # 337
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation
NEW QUESTION # 338
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
1- Regression
2- Clustering
3- Classification
NEW QUESTION # 339
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