AI-900 Online Practice Questions and Answers

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

Correct Answer:

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

Correct Answer:

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

Correct Answer:

Reference: https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/

Correct Answer:

Correct Answer: AC

Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.

Reference:

https://www.azure.cn/en-us/pricing/details/machine-learning/

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

Correct Answer: B

QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base--automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. Incorrect Answers:

A: 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.

D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

Correct Answer: A

Understand steps for clustering

You can think of the steps to train and evaluate a clustering machine learning model as:

1.

Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.

2.

Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.

3.

Evaluate performance.

4.

Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.

Reference: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model

Correct Answer: D

The Cognitive Services Speech SDK provides two ways to recognize intents. An intent is something the user wants to do: book a flight, check the weather, or make a call. Using intent recognition, your applications, tools, and devices can determine what the user wants to initiate or do based on options you define in the Intent Recognizer or Conversational Language Understanding (CLU) model.

1.

Pattern matching

The Speech SDK provides an embedded pattern matcher that you can use to recognize intents in a very strict way. This is useful for when you need a quick offline solution. This works especially well when the user is going to be trained in

some way or can be expected to use specific phrases to trigger intents.

2.

Conversational Language Understanding

Conversational language understanding (CLU) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it.

Both a Speech resource and Language resource are required to use CLU with the Speech SDK. The Speech resource is used to transcribe the user's speech into text, and the Language resource is used to recognize the intent of the utterance.

Reference: https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/intent-recognition

Correct Answer: C

Explanation:

Brand detection is a specialized mode of object detection that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular

on social media or most prevalent in media product placement.

The Azure AI Vision service detects whether there are brand logos in a given image; if there are, it returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.

The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for is not detected by the Azure AI Vision service, you could also try creating and training your own logo

detector using the Custom Vision service.

Reference:

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection