AI900

Which Azure AI Service for Language feature allows you to analyze written articles to extract information and concepts, such as people and locations, for classification purposes?

named entity recognition

Which feature of the Azure AI Language service includes functionality that returns links to external websites to disambiguate terms identified in a text?

entity recognition

At which layer can you apply content filters to suppress prompts and responses for a responsible generative AI solution?

Safety System

System messages

System messages should be used to set the context for the model by describing expectations. Based on system messages, the model knows how to respond to prompts. The other techniques are also used in generative AI models, but for other use cases.

As per the NIST AI Risk Management Framework, what is the first stage to consider when developing a responsible generative AI solution?

Identify potential harms.

Which natural language processing (NLP) workload is used to generate closed caption text for live presentations?

Azure AI Speech

Which principle of responsible artificial intelligence (AI) involves evaluating and mitigating the bias introduced by the features of a model?

Fairness

Which principle of responsible artificial intelligence (AI) defines the framework of governance and organization principles that meet ethical and legal standards of AI solutions?

Accountability

Which principle of responsible artificial intelligence (AI) plays the primary role when implementing an AI solution that meet qualifications for business loan approvals?

Fairness is meant to ensure that AI models do not unintentionally incorporate a bias based on criteria such as gender or ethnicity.

Which principle of responsible artificial intelligence (AI) is applied in the design of an AI system to ensure that users understand constraints and limitations of AI?

Transparency

Clustering

A clustering algorithm is an example of unsupervised learning, which groups data points that have similar characteristics without relying on training and validating label predictions. Supervised learning is a category of learning algorithms that includes regression and classification, but not clustering. Classification and regression algorithms are examples of supervised machine learning.

You plan to use machine learning to predict the probability of humans developing diabetes based on their age and body fat percentage. What should the model include?

Two features and one label The scenario represents a model that is meant to establish a relationship between two features (age and body fat percentage) and one label (the likelihood of developing diabetes). The features are descriptive attributes (serving as the input), while the label is the characteristic you are trying to predict (serving as the output).

In a regression machine learning algorithm, how are features and labels handled in a validation dataset?

In a regression machine learning algorithm, features are used to generate predictions for the label, which is compared to the actual label value. There is no direct comparison of features or labels between the validation and training datasets.

A company is using machine learning to predict house prices based on appropriate house attributes. For the machine learning model, which attribute is the label?

The price of the house is the label you are attempting to predict through the machine learning model. This is typically done by using a regression model. Floor space size, number of bedrooms, and age of the house are all input variables for the model to help predict the house price label.

You need to use Azure Machine Learning to train a regression model. What should you create in Machine Learning studio?

Job

You need to use the Azure Machine Learning designer to train a machine learning model. What should you do first in the Machine Learning designer?

Before you can start training a machine learning model, you must first create a pipeline in the Machine Learning designer. This is followed by adding a dataset, adding training modules, and eventually deploying a service.

You need to use the Azure Machine Learning designer to deploy a predictive service from a newly trained model. What should you do first in the Machine Learning designer?

To deploy a predictive service from a newly trained model by using the Machine Learning designer, you must first create a pipeline in the Machine Learning designer. Adding training modules by using the Machine Learning designer takes place before creating a trained model, which already exists. Adding a dataset by using the Machine Learning designer requires that a pipeline already exists. To create an inferencing cluster, you must use Machine Learning studio.

Models are trained using data, which may include personal information. AI developers have a responsibility to ensure that the training data is kept secure, and that the trained models themselves can't be used to

Privacy and Security

Which metric can you use to evaluate a classification model?

True Positive rate

Which two components can you drag onto a canvas in azure machine learning designer?

Dataset Module

Classification

True or False Yes or No

Ensuring that the numeric variables in training data are on a similar scale is an example of

Feature Selection

A ___________ is a workflow you build to manage the data and modules used to train and evaluate a machine learning model.

Pipeline

Features

What do i know

Labels

What do I want to predict? (AKA Answer)

Write Custom code on Azure ML Designer language

Python R

In Azure Cognitive services, what is a QnA Knowledge basw

A dataset that includes questions and answer pairs

Semantic Segmentation

Determines which pixels in an image are part of a bear

Facial Recognition Task: Verification

Do two images of a face belong to the same person?

Facial Recognition Task: Similarity

Does this person look like other people?

Facial Recognition Task: Grouping

Do all the faces belong together?

Facial Recognition Task: Identification

Who is this person in this group of people?

While presenting at a conference, your session is transcribed into subtitles for the audience. This is an example of

Speech Recognition

You can use the Speech services to translate the audio of a call into a different language

True

Speech Synthesis

Is pretty much a fake voice. Text to lifelike speech

Entity Recognition

Can identify people, places, organizations. date/time, quantities, percentages, currencies etc.

Utterance

The NONE intent is filled with utterances that are outside of your domain

Language Service

Can identify companies and organization mentioned in a document

Deep Learning

A process on experience and data using mathematical algorithms.

Azure AI Vision Image Analysis

Provides tags and categorizes images, which is ideal for categorizing user-uploaded images.

ChatGPT Chain Prompting

involves asking a new prompt that relies on the answer given in a previous prompt

Note

More token capacity = slower completion time

Azure AI Face Services: one-to-many

Face Identification

Azure AI Face Services: one-to-one

Face Verification

Azure AI Video Indexer

A cloud application. It enables you to extract the insights from your videos using AI video indexer video and audio models. - Uses 30+ AI models, generating rich Insights