AI-900 Online Practice Questions and Answers

Correct Answer:

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

Correct Answer:

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

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0

Correct Answer:

Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/5-create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/1-introduction

Correct Answer:

Box: 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.

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

Correct Answer:

Box 1: Reliability and safety To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.

Box 2: Fairness Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.

We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held accountable for consequential decisions that affect others.

Box 3: Privacy and security As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used

Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Correct Answer:

Box 1: Regression

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.

Box 2: Classification

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

Box 3: Clustering 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/studio-module-reference/linear-regression

Correct Answer:

Box 1: Speech

Custom Speech: Code-free automated machine learning for speech recognition

Speech to text is a Speech service feature that accurately transcribes spoken audio to text.

Make spoken audio actionable

Quickly and accurately transcribe audio to text in more than 100 languages and variants. Customize models to enhance accuracy for domain-specific terminology. Get more value from spoken audio by enabling search or analytics on

transcribed text or facilitating action—all in your preferred programming language.

Box 2: Language service

Add intents to your LUIS app to identify groups of questions or commands that have the same intention.

Note: Language understanding (LU) is a very centric component to enable conversational services such as bots, IoT experiences, analytics, and others. In a spoken dialog system, LU converts from the words in a sentence into a machine-

readable meaning representation, typically indicating the intent of the sentence and any present entities. For example, consider a physical fitness domain, with a dialog system embedded in a wearable device like a watch. This dialog system

could recognize intents like StartActivity and StopActivity, and could recognize entities like ActivityType. In the user input “begin a jog”, the goal of LU is to identify the intent as StartActivity, and identify the entity ActivityType= ’’jog’’.

Box 3: Language service

Intent compared to entity

The intent represents the action the application should take for the user, based on the entire utterance. An utterance can have only one top-scoring intent, but it can have many entities.

Create an intent when the user's intention would trigger an action in your client application, like a call to the checkweather() function from the table above. Then create entities to represent parameters required to execute the action.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text

https://azure.microsoft.com/en-us/blog/luis-ai-automated-machine-learning-for-custom-language-understanding/

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/concepts/intents

Correct Answer:

Box 1: Speech

The Speech service provides speech-to-text and text-to-speech capabilities with an Azure Speech resource. You can transcribe speech to text with high accuracy, produce natural-sounding text-to-speech voices, translate spoken audio, and

use speaker recognition during conversations.

Box 2: Language service

Build applications with conversational language understanding, a Cognitive Service for Language feature that understands natural language to interpret user goals and extracts key information from conversational phrases. Create multilingual,

customizable intent classification and entity extraction models for your domain-specific keywords or phrases across 96 languages.

Box 3: Speech

Incorrect:

Not Translator text: Text translation is a cloud-based REST API feature of the Translator service that uses neural machine translation technology to enable quick and accurate source-to-target text translation in real time across all supported

languages.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overview

https://azure.microsoft.com/en-us/services/cognitive-services/conversational-language-understanding/

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

Correct Answer: C

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.

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

Correct Answer: A

Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/

Correct Answer: C

Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents. Reference: https://azure.microsoft.com/en-us/services/form-recognizer/

Correct Answer: D

You can use OCR to Turn PDF and Image Files into Electronic Documents.

Reference: https://learn.microsoft.com/en-us/dynamics-nav-app/across-how-use-ocr-pdf-images-files

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: C