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The Microsoft AI-900 Exam covers various topics such as Azure Cognitive Services, Azure Machine Learning, and other AI-related technologies. AI-900 exam is designed to test individuals' knowledge of AI concepts, machine learning algorithms, and data processing techniques. AI-900 exam also tests knowledge on how to implement AI solutions on Azure and how to use Azure tools and services for AI development.
This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.
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Microsoft AI-900 exam is designed to evaluate an individual's understanding of AI and ML concepts, such as machine learning types, data labeling, and the creation of ML models using Azure services. AI-900 exam also covers the Azure AI services and their capabilities, including Cognitive Services, Azure Machine Learning, and Bot Services. The AI-900 Exam is suitable for individuals who have a basic understanding of cloud computing and programming concepts.
NEW QUESTION # 18
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and-manage-intelligent-bots
NEW QUESTION # 19
You need to build an image tagging solution for social media that tags images of your friends automatically.
Which Azure Cognitive Services service should you use?
Answer: A
Explanation:
The correct answer is B. Face because the Azure Face service, part of Azure Cognitive Services, provides capabilities for detecting, recognizing, and analyzing human faces in images. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore computer vision", the Face service can automatically detect faces, determine attributes such as age, gender, emotion, and identify or verify individuals based on facial features.
In this scenario, the goal is to build an image tagging solution that automatically tags images of friends on social media. This requires the ability to recognize and match faces of known individuals, which is a key feature of the Face API. The API uses facial recognition technology to create face IDs for detected faces and can compare new faces against stored face data to identify known individuals. Microsoft Learn notes: "The Face service can detect, recognize, and identify people in images, enabling automated tagging or authentication scenarios." Other options explained:
* A. Computer Vision identifies objects, landmarks, and general content but does not specialize in identifying individual people.
* C. Text Analytics analyzes textual data for sentiment, key phrases, or entities, not image data.
* D. Form Recognizer extracts structured data from forms or receipts, unrelated to face detection.
Thus, for automatically tagging people in photos, Azure Face is the correct and most suitable service.
NEW QUESTION # 20
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore computer vision", Optical Character Recognition (OCR) is a form of computer vision that enables a system to detect and extract printed or handwritten text from images or documents. OCR is particularly useful in scenarios where the goal is to digitize textual information from physical documents, such as receipts, invoices, or travel expense forms - exactly as described in this question.
In the given scenario, employees need a mobile application that allows them to scan and store expenses while traveling. The process involves taking photos of receipts that contain printed text, such as vendor names, totals, dates, and item descriptions. The OCR technology automatically detects the text areas within the image and converts them into machine-readable and searchable data that can be stored in a database or processed further for expense management.
Microsoft's Azure Cognitive Services include the Computer Vision API and the Form Recognizer service, both of which use OCR technology. The Form Recognizer builds upon OCR by adding intelligent document understanding, enabling it to extract structured data from expense receipts automatically.
Other answer options are incorrect for the following reasons:
* A. Semantic segmentation assigns labels to every pixel in an image, typically used in autonomous driving or medical imaging, not for text extraction.
* B. Image classification identifies the overall category of an image (e.g., "This is a receipt"), but it does not extract the textual content.
* C. Object detection identifies and locates objects in an image with bounding boxes but is not used for text reading or conversion.
Therefore, based on the official AI-900 training and Microsoft Learn content, the correct answer is D. Optical Character Recognition (OCR) - the technology that enables extracting textual information from scanned expense receipts.
NEW QUESTION # 21
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
Answer: B
Explanation:
Explanation
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
NEW QUESTION # 22
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 23
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