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Microsoft AI-900 exam, also known as the Microsoft Azure AI Fundamentals exam, is a certification exam that tests individuals on their knowledge of artificial intelligence and its applications in Microsoft Azure. AI-900 exam is designed for individuals who are interested in learning about AI and its practical use in the cloud, and for those who are looking to gain a basic understanding of AI terminology, concepts and technologies.
The Microsoft AI-900 Exam consists of 40-60 multiple-choice questions and has a time limit of 60 minutes. The passing score for the exam is 700 out of 1000. To prepare for the exam, Microsoft offers a variety of resources, including online training courses, practice exams, and study guides.
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NEW QUESTION # 331
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 # 332
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
B: Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses the latest models and works with text on a variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two OCR APIs support extracting printed text in several languages.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
NEW QUESTION # 333
You need to convert handwritten notes into digital text.
Which type of computer vision should you use?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation on Azure AI Vision, OCR is a computer vision technology that detects and extracts printed or handwritten text from images, scanned documents, or photographs. The OCR feature in Azure AI Vision can analyze images containing handwritten notes, recognize the characters, and convert them into machine-readable digital text.
This process is ideal for digitizing handwritten meeting notes, forms, or classroom materials. OCR works by identifying text regions in an image, segmenting characters or words, and then applying language models to interpret them correctly. Azure's OCR capabilities support multiple languages and can handle varied handwriting styles.
Other options are incorrect because:
* B. Object detection identifies and locates objects (like cars, animals, or furniture) within an image, not text.
* C. Image classification assigns an image to a predefined category (e.g., "dog" or "cat") rather than extracting text.
* D. Facial detection detects or recognizes human faces, not written text.
Therefore, to convert handwritten notes into digital text, the correct computer vision technique is Optical Character Recognition (OCR).
NEW QUESTION # 334
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore Azure Machine Learning", when you publish an inference pipeline (a deployed web service for real-time predictions) using Azure Machine Learning designer, you make the model accessible as a RESTful endpoint. Consumers-such as applications, scripts, or services-interact with this endpoint to submit data and receive predictions.
To securely access this deployed pipeline, two critical parameters are required:
* REST endpoint (Option D):The REST endpoint is a URL automatically generated when the inference pipeline is deployed. It defines the network location where clients send HTTP POST requests containing input data (usually in JSON format). The endpoint routes these requests to the deployed model, which processes the data and returns prediction results. The REST endpoint acts as the primary access point for consuming the model's inferencing capability programmatically.
* Authentication key (Option C):The authentication key (or API key) is a security token provided by Azure to ensure that only authorized users or systems can access the endpoint. When invoking the REST service, the key must be included in the request header (typically as the value of the Authorization header). This mechanism enforces secure, authenticated access to the deployed model.
The other options are incorrect:
* A. The model name is not required to consume the endpoint; it is used internally within the workspace.
* B. The training endpoint is used for training pipelines, not for inference.
Therefore, according to Microsoft's official AI-900 learning objectives and Azure Machine Learning documentation, when consuming a published inference pipeline, you must use both the REST endpoint (D) and the authentication key (C). These parameters ensure secure, controlled, and programmatic access to the deployed AI model for real-time predictions.
NEW QUESTION # 335
You need to implement a pre-built solution that will identify well-known brands in digital photographs. Which Azure Al sen/tee should you use?
Answer: B
NEW QUESTION # 336
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