Which Azure AI Vision feature can you use to generate automatic captions for digital photographs?
Answer(s): D
Describe images with human-readable languageComputer Vision can analyze an image and generate a human-readable phrase that describes its contents.The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.The image description feature is part of the Analyze Image API.
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-describing- images
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents.
https://vidado.ai/handwriting-ocr
HOTSPOT (Drag and Drop is not supported)For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: YesImage classification is a popular area of artificial intelligence. One application of image classification that's already being used in industry is the detection of quality issues on assembly lines during manufacturing. In a typical production line, components travel down the assembly line from one station to another, at the end of which an inspector steps in to look for problems--a manual and error-prone process. AI-driven image classification reduces human effort and automatically classifies images as pass or fail. This improves not only the efficiency of the human operators in the validation process, but also the quality of the overall manufacturing process.Box 2: YesObject detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat and person, the Detect operation will list those objects 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 object in an image.Box 3: No
https://azure.microsoft.com/en-us/use-cases/defect-detection-with-image-analysis/ https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
Box 1: Facial analysis.In another change, we [Microsoft] will retire facial analysis capabilities that purport to infer emotional states and identity attributes such as gender, age, smile, facial hair, hair, and makeup. We collaborated with internal and external researchers to understand the limitations and potential benefits of this technology and navigate the tradeoffs. In the case of emotion classification specifically, these efforts raised important questions about privacy, the lack of consensus on a definition of "emotions," and the inability to generalize the linkage between facial expression and emotional state across use cases, regions, and demographics. API access to capabilities that predict sensitive attributes also opens up a wide range of ways they can be misused -- including subjecting people to stereotyping, discrimination, or unfair denial of services.
https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial- recognition/300
HOTSPOT (Drag and Drop is not supported)To complete the sentence, select the appropriate option in the answer area.Hot Area:
Box 1: detection.Attributes are a set of features that can optionally be detected by the Face - Detect API. The following attributes can be detected:* Exposure. The exposure of the face in the image. This attribute returns a value between zero and one and an informal rating of underExposure, goodExposure, or overExposure.* Noise. The visual noise detected in the face image. This attribute returns a value between zero and one and an informal rating of low, medium, or high.* Occlusion. Whether there are objects blocking parts of the face. This attribute returns a Boolean value for eyeOccluded, foreheadOccluded, and mouthOccluded.* Etc.
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-face-detection
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