AI-900 Exam Dumps - Microsoft Azure AI Fundamentals

Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.

The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.

This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.

Here’s how it works:

Developers upload FAQ documents, URLs, or structured content.

Azure AI Language processes the content and identifies logical question-answer pairs.

The model stores these pairs in a knowledge base that can be queried by user input.

When users ask questions, the model finds the best matching answer using natural language understanding techniques.

In contrast:

Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.

Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.

Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.

Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.

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

You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.

https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

Box 1: 11

TP = True Positive.

The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).

Box 2: 1,033

FN = False Negative

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of natural language processing (NLP) workloads on Azure,” sentiment analysis is an NLP workload that determines the emotional tone or opinion expressed in a piece of text. This could be positive, negative, or neutral sentiment.

When an app analyzes social media posts to identify their tone, it is performing sentiment analysis, since it aims to understand the emotional context behind user-generated text such as tweets, reviews, or comments. Azure provides this functionality through the Azure Cognitive Services – Text Analytics API, which evaluates text and returns sentiment scores.

Other options are not suitable:

Key phrase extraction identifies main ideas in text but not tone.

Entity recognition identifies names of people, organizations, or locations.

Speech recognition converts spoken words into text, not emotional analysis.

Therefore, analyzing social media tone is an example of sentiment analysis, a key NLP workload in Microsoft’s AI-900 syllabus.

“features.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe fundamental principles of machine learning on Azure,” in a machine learning model, the data used as inputs are known as features, while the data that represents the output or target prediction is known as the label.

Features are measurable attributes or properties of the data used by a model to learn patterns and make predictions. They are also referred to as independent variables because they influence the result that the model tries to predict. For example, in a machine learning model that predicts house prices:

Features might include square footage, location, and number of bedrooms, while

The label would be the house price (the value being predicted).

In the context of Azure Machine Learning, during model training, features are passed into the algorithm as input variables (X-values), and the label is the corresponding output (Y-value). The model then learns the relationship between the features and the label.

Let’s review the incorrect options:

Functions: These are mathematical operations or relationships used inside algorithms, not the input data itself.

Labels: These are the outputs or results that the model predicts, not the inputs.

Instances: These refer to individual data records or rows in the dataset, not the input fields themselves.

Hence, in any supervised or unsupervised learning process, the input data (independent variables) are called features, and the model uses them to predict labels (dependent variables).

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.

The correct answers are B and D.

Speech recognition, part of Azure’s Speech service, converts spoken audio into written text. It is a core feature of Azure Cognitive Services for speech-to-text scenarios.

Providing closed captions for recorded or live videos (B) – This is a typical application of speech recognition. The AI system listens to audio content from a video and generates real-time or post-event captions. Azure’s Speech-to-Text API is frequently used in broadcasting and video platforms to improve accessibility and searchability.

Creating a transcript of a telephone call or meeting (D) – Another common use case is automated transcription. The Speech service can process real-time audio streams (such as meetings or calls) and produce accurate text transcripts. This is widely used in customer service, call analytics, and meeting documentation.

The incorrect options are:

A. an in-car system that reads text messages aloud – This uses Text-to-Speech, not speech recognition.

C. creating an automated public address system for a train station – This also uses Text-to-Speech, since it generates spoken output from text.

Therefore, scenarios that convert spoken words into text correctly represent speech recognition, making B and D the right answers.