AI-900 Online Practice Questions and Answers

Correct Answer:

In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.

In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

Incorrect Answers:

Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable

inputs. Narrowing the field of data helps reduce noise and improve training performance.

Reference:

https://www.cloudfactory.com/data-labeling-guide

Correct Answer:

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:

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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.

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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.

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Occlusion. Whether there are objects blocking parts of the face. This attribute returns a Boolean value for eyeOccluded, foreheadOccluded, and mouthOccluded.

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Etc.

Reference: https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-face-detection

Correct Answer:

Box: Computer Vision Cognitive Services, Computer Vision, Object detection example Object 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.

Reference: https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Correct Answer: CD

Correct Answer: AB

Use Azure Machine Learning designer for customizing using Python and R code. Reference: https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

Correct Answer: D

Correct Answer: C

Correct Answer: C

Embed facial recognition into your apps for a seamless and highly secured user experience. No machine-learning expertise is required. Features include face detection that perceives facial features and attributes — such as a face mask, glasses, or face location — in an image, and identification of a person by a match to your private repository or via photo ID.

Note: Facial recognition CCTV cameras can take images of faces they film and compare them against a predefined database of images to determine if there is a match.

Reference: https://azure.microsoft.com/en-in/products/cognitive-services/face

Correct Answer: C

Natural language processing (NLP) has many uses: sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

Need translation as different languages are used.

Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase

extraction to quickly identify the main concepts in text.

Reference:

https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview

Correct Answer: C