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| Section | Weight | Objectives |
|---|---|---|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
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NEW QUESTION # 147
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION # 148
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct mappings are based on the Microsoft Azure AI Fundamentals (AI-900) curriculum topic:
"Describe features of computer vision workloads." Microsoft divides computer vision tasks into key workload types - image classification, object detection, facial recognition, and optical character recognition (OCR) - each designed for specific visual analysis objectives.
* Identify celebrities in images # Facial recognitionFacial recognition goes beyond simple face detection; it can identify or verify specific individuals by comparing facial features with known profiles.
According to Microsoft Learn, the Face service in Azure Cognitive Services can detect, recognize, and identify people in photos or videos. Recognizing celebrities or known individuals is a prime example of facial recognition.
* Extract movie title names from movie poster images # Optical Character Recognition (OCR)OCR is used to detect and extract text content from images, such as printed or handwritten words. Azure's Computer Vision API uses OCR technology to read text in various languages from photos, scanned documents, or posters. Therefore, extracting movie titles or actor names from a poster image is a perfect use case for OCR.
* Locate vehicles in images # Object detectionObject detection identifies and locates specific objects within an image, returning bounding boxes that indicate their positions. In Azure, the Custom Vision service or Computer Vision object detection models are used to detect multiple objects like vehicles, pedestrians, or animals in a single image.
Summary:
* Facial recognition # Identifies specific people (celebrities)
* OCR # Extracts text (movie titles)
* Object detection # Finds and locates physical items (vehicles)
Thus, the verified and official answer is:
NEW QUESTION # 149
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 150
You have the following apps:
* App1: Uses a set of images and photos to extract brand names
* App2: Enables touchless access control for buildings
Which Azure Al Vision service does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure AI Vision services, different Azure AI Vision capabilities are suited for different use cases such as object detection, brand recognition, facial recognition, and spatial analysis.
* App1: Uses a set of images and photos to extract brand names # Image AnalysisThe Azure AI Vision - Image Analysis service (formerly part of Computer Vision) can detect and extract brands, objects, text, and other visual features from images. It uses advanced image classification and object detection models to recognize logos and identify brand names (for example, "Microsoft" or "Coca-Cola") in photos. The Image Analysis API can also return descriptive tags, scene descriptions, and confidence scores. Therefore, since App1 analyzes static images to extract brand names, it specifically relies on the Image Analysis feature of Azure AI Vision.
* App2: Enables touchless access control for buildings # FaceThe Azure AI Face service is designed for facial detection, verification, and identification. It can recognize and match faces in real time, making it ideal for access control, identity verification, and attendance tracking systems. A "touchless access control" system uses a camera to detect a person's face and verify identity against a stored profile, allowing or denying entry without physical interaction.
The other options are not suitable:
* Optical Character Recognition (OCR) extracts text, not brand logos.
* Spatial Analysis is for detecting movement or presence in video feeds.
* Video Analysis is for analyzing dynamic video content rather than still images.
NEW QUESTION # 151
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 152
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