Microsoft AI-900 Practice Test - Questions Answers, Page 7

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python

https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.

Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.

Incorrect Answers:

Computer Vision:

Azure's Computer Vision service provides developers with 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.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection

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

The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.

Box 1: Yes

You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.

Box 2: No

Box 3: Yes

Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

Box 1: Yes

Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.

The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.

Box 2: No

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.

Box 3: Yes

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

Reference:

https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing