Free Practice Questions for Microsoft AI-900 Exam

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore natural language processing (NLP) in Azure,” sentiment analysis is a core natural language processing (NLP) workload used to determine the emotional tone or attitude expressed in a piece of text. It helps identify whether a statement, review, or comment conveys a positive, negative, neutral, or mixed sentiment.

In this question, the scenario involves analyzing customer reviews and determining how positive or negative each review is. This directly aligns with sentiment analysis, which evaluates subjective text and quantifies the expressed opinion. In Azure, this workload is implemented through the Azure AI Language service (formerly Text Analytics API), where the Sentiment Analysis feature assigns a sentiment score to text inputs and classifies them accordingly.

For example:

“I love this product!” → Positive sentiment

“It’s okay, but could be better.” → Neutral or mixed sentiment

“I’m disappointed with the service.” → Negative sentiment

Let’s analyze why the other options are incorrect:

A. Language detection: Identifies which language (e.g., English, Spanish, French) the text is written in. It doesn’t measure positivity or negativity.

C. Key phrase extraction: Identifies the main topics or keywords in text (e.g., “battery life,” “customer support”), not the emotion.

D. Entity recognition: Detects and categorizes specific entities such as people, locations, organizations, or dates within the text.

Therefore, based on Microsoft’s AI-900 syllabus and Azure AI Language documentation, the workload that analyzes text to determine positive or negative opinions is Sentiment Analysis (Option B). This capability is widely used in customer feedback analysis, brand monitoring, and social media analytics to understand public perception and improve business decisions.

According to the Microsoft Learn module “Explore fundamental principles of machine learning” and the AI-900 Official Study Guide, when building and evaluating a model (such as a clustering model) in Azure Machine Learning designer, data must be divided into two subsets:

Training dataset: Used to train the model so it can learn patterns and relationships in the data.

Testing dataset: Used to evaluate the model’s performance on unseen data, ensuring that it generalizes well and does not overfit.

In Azure ML Designer, this is typically done using the Split Data module, which separates the dataset into training and testing portions (for example, 70% training and 30% testing). After training, you connect the testing dataset to an Evaluate Model module to assess metrics such as accuracy, precision, or silhouette score (for clustering).

Other options are incorrect:

A. Split into features and labels: Clustering is an unsupervised learning technique, so it doesn’t use labeled data.

B. Use training dataset for evaluation: This would cause overfitting, as the model is being tested on the same data it learned from.

D. Use the original dataset for training and evaluation: Also causes overfitting, offering no measure of generalization.

The correct completion of the sentence is:

“The interactive answering of questions entered by a user as part of an application is an example of natural language processing.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, Natural Language Processing (NLP) is a branch of Artificial Intelligence that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful. It is one of the key AI workloads described in the “Describe features of common AI workloads” module on Microsoft Learn.

When a user types a question into an application and the system responds interactively — such as in a chatbot, Q & A system, or virtual assistant — this process requires language understanding. NLP allows the system to process the input text, determine user intent, extract relevant entities, and generate an appropriate response. This is the foundational capability behind services such as Azure Cognitive Service for Language, Language Understanding (LUIS), and QnA Maker (now integrated as Question Answering in the Language service).

Microsoft’s study guide explains that NLP workloads include the following key scenarios:

Language understanding: Determining intent and context from text or speech.

Text analytics: Extracting meaning, key phrases, sentiment, or named entities.

Conversational AI: Powering bots and virtual agents to interact using natural language.These systems rely on NLP models to analyze user inputs and respond accordingly.

In contrast:

Anomaly detection identifies data irregularities.

Computer vision analyzes images or video.

Forecasting predicts future values based on historical data.

Therefore, based on the AI-900 official materials, the interactive answering of user questions through an application clearly falls under Natural Language Processing (NLP).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of common AI workloads and considerations”, Microsoft has defined six guiding principles for responsible AI. These principles are intended to ensure that AI systems are developed and deployed in ways that are ethical, transparent, and beneficial to all. The six principles are: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability.

Let’s break down the three correct options:

Fairness – Microsoft emphasizes that AI systems should treat all individuals fairly and avoid discrimination against people based on gender, race, age, or other characteristics. Fairness ensures that outcomes and decisions from AI systems are equitable across diverse user groups. In the AI-900 learning materials, fairness is explained as a foundational value that ensures algorithms and models do not introduce or amplify societal bias.

Reliability and Safety – This principle ensures that AI systems function as intended under all expected conditions and that they can handle unexpected inputs safely. Microsoft states that AI should be tested rigorously and validated for reliability before deployment. AI systems must perform consistently and avoid causing harm due to errors or failures.

Inclusiveness – Inclusiveness focuses on empowering everyone and engaging people of all backgrounds. Microsoft’s responsible AI guidance stresses designing AI systems that understand and respect cultural, linguistic, and ability differences to make technology accessible and beneficial to all users.

Options A (knowledgeability), B (decisiveness), and E (opinionatedness) are not part of Microsoft’s Responsible AI principles. These terms do not appear in any Microsoft Learn AI-900 curriculum or official responsible AI documentation.

Thus, based on the verified AI-900 study content and Microsoft’s Responsible AI framework, the correct answer is C. Inclusiveness, D. Fairness, and F. Reliability and Safety.

The Azure OpenAI DALL-E model is specifically designed for generating original images from natural language prompts. If you want to create an image of a “rural scene,” you can use Azure AI Foundry (formerly Azure OpenAI Studio) to deploy the DALL-E model and provide descriptive instructions such as “create an image of a peaceful rural village with trees and a sunset.”

A. GPT-3.5 Turbo → Handles text generation, not image creation.

B. Bing search → Finds existing images, not generate original ones.

C. GitHub Copilot → Assists with writing code, not generating images.

Box1: Sentiment analysis

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

Box 2: Broad entity extraction

Broad entity extraction: Identify important concepts in text, including key

Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.

Box 3: Entity Recognition

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.

Yes, Yes, and No.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module “Identify features of natural language processing (NLP) workloads on Azure”, the Azure Translator service is a cloud-based AI service within Azure Cognitive Services that provides real-time text translation across multiple languages.

“You can use the Translator service to translate text between languages.” – Yes.This is the core function of the Translator service. It takes text as input in one language and returns it in another using advanced neural machine translation models. This aligns with the AI-900 learning objective: “Describe the capabilities of Azure Cognitive Services for language”, which specifically names Azure Translator as the service used to perform automatic text translation. The service supports over 100 languages and dialects, offering both single-sentence and document-level translations.

“You can use the Translator service to detect the language of a given text.” – Yes.This statement is also true. The Translator service automatically detects the source language if it is not specified in the request. This feature is documented in the Azure Translator API, where the system identifies the input language before performing translation. The AI-900 exam content emphasizes this as one of the Translator service’s built-in capabilities—language detection for untagged text.

“You can use the Translator service to transcribe audible speech into text.” – No.This is not a function of Translator. Transcription (converting speech to text) is a speech AI workload, handled by the Azure Speech Service, not Translator. The Speech-to-Text capability in Azure Cognitive Services processes spoken audio input and returns the text transcription. The Translator service only works with text input, not direct audio.

Therefore, based on official AI-900 guidance, the verified configuration is:

✅ Yes – for text translation

✅ Yes – for language detection

❌ No – for speech transcription.

This aligns precisely with the AI-900 learning outcomes describing Text Translation and Language Detection as Translator capabilities, and Speech Transcription as part of the separate Speech service.

When interacting with the Azure OpenAI REST API, requests and responses are structured using the JSON format. According to the Microsoft Learn documentation on the Azure OpenAI REST API, JSON is the required format for sending prompts, configuration parameters (such as temperature, max_tokens, or top_p), and metadata to the API endpoint.

A typical request to an Azure OpenAI endpoint (for example, /openai/deployments/{model-name}/chat/completions?api-version=2024-02-01) includes a JSON body similar to the following:

{

" messages " : [

{ " role " : " system " , " content " : " You are an assistant. " },

{ " role " : " user " , " content " : " Write a JavaScript function to add two numbers. " }

],

" max_tokens " : 200,

" temperature " : 0.7

}

This JSON format allows for structured data exchange between the client and the server, ensuring that key-value pairs are properly parsed by the API.

Option A (CSV): Used for storing tabular data, not suitable for API communication.

Option C (XML): Though historically used in web services, Microsoft’s modern APIs, including Azure OpenAI, rely on JSON.

Option D (YAML): Common in configuration files but not for REST API payloads.

Therefore, per official Microsoft documentation and the AI-900 study guide, the correct and verified answer is B. JSON, as it is the standard format required when sending requests to the Azure OpenAI REST API endpoint.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” regression is a type of supervised machine learning used to predict continuous numeric values.

In this question, the goal is to predict how many vehicles will travel across a bridge on a given day. The predicted output (the number of vehicles) is a continuous value—meaning it can take on any numerical value depending on various factors like time, weather, or day of the week. This makes it a regression problem, as the model learns from historical numeric data to estimate a continuous outcome.

How Regression Works:

Regression models find patterns between input features (such as temperature, weekday/weekend, traffic trends) and a numerical output (number of vehicles). Common regression algorithms include linear regression, decision trees for regression, and neural network regression. In Azure Machine Learning, regression tasks are used for business scenarios such as:

Predicting sales revenue for a future month.

Estimating house prices based on property characteristics.

Forecasting energy consumption or traffic flow, as in this case.

Why not the other options?

Classification: Used for predicting discrete categories (e.g., “spam” vs. “not spam”). It does not handle continuous numeric values.

Clustering: An unsupervised learning technique used to group data points based on similarity without predefined labels (e.g., segmenting customers into groups).

Therefore, the task of predicting the number of vehicles—a numeric, continuous value—is a regression problem.

This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules “Explore natural language processing” and “Explore speech capabilities.” These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.

You can use the Speech service to transcribe a call to text → YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains: “The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files.” This makes it ideal for call transcription, voice assistants, and meeting captioning.

You can use the Text Analytics service to extract key entities from a call transcript → YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: “Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data.”

You can use the Speech service to translate the audio of a call to a different language → YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language. Microsoft Learn describes this as: “Speech Translation combines speech recognition and translation to translate spoken audio to another language.”