AI-900 Online Practice Questions and Answers

Correct Answer:

In the most basic sense, regression refers to prediction of a numeric target.

Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.

You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Incorrect Answers:

1.

Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

2.

Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.

Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

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

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

Correct Answer:

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: C

A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2

Correct Answer: D

Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text

Correct Answer: C

Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

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

Correct Answer: A

The Azure Face service provides AI algorithms that detect, recognize, and analyze human faces in images. Facial recognition software is important in many different scenarios, such as identity verification, touchless access control, and face blurring for privacy.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/howtodetectfacesinimage

Correct Answer: D

What is custom named entity recognition (NER)?

Custom NER is one of the custom features offered by Azure Cognitive Service for Language. It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for custom named entity recognition

tasks.

Custom NER enables users to build custom AI models to extract domain-specific entities from unstructured text, such as contracts or financial documents.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/custom-named-entity-recognition/overview