Free Microsoft AI-900 Practice Exam with Questions & Answers | Set: 4

Before building or training any model in Azure Machine Learning Studio—including when using Automated ML (AutoML)—you must first create a Machine Learning workspace.

A workspace serves as the central environment for all machine learning assets such as datasets, compute targets, models, pipelines, and experiments. According to the AI-900 study guide and Microsoft Learn module “Describe features and tools for machine learning in Azure,” a workspace is the foundational setup required to organize and manage all ML-related resources.

The sequence typically follows these steps:

Create a Machine Learning workspace.

Configure compute resources (e.g., compute instance or cluster).

Upload or register datasets.

Use Automated ML or Designer to train models.

Deploy and manage the trained models.

Option A (Jupyter notebook) is an optional tool for coding experiments.

Option C (Registered dataset) is created after the workspace exists.

Option D (Designer pipeline) is a visual tool used within the workspace.

Hence, B. a Machine Learning workspace is the correct answer because it is the first and mandatory step before using Automated ML or any training component in Azure Machine Learning Studio.

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 supervised machine learning technique used to predict continuous numeric values based on input data.

In this scenario, the goal is to predict how many hours of overtime a delivery person will work depending on the number of orders received. The output — the number of overtime hours — is a continuous variable (for example, 1.5 hours, 3.2 hours, etc.), not a category. This makes it a regression problem, where the model learns patterns from historical data and uses those patterns to estimate a continuous numeric outcome.

Why Regression Applies Here:

Regression models work by finding the mathematical relationship between input features (independent variables) and output values (dependent variables). In this case:

Input (feature): Number of orders received

Output (label): Predicted overtime hours

Azure Machine Learning supports several regression algorithms, including Linear Regression, Decision Tree Regression, and Neural Network Regression, all of which can handle scenarios where a numeric prediction is required.

Why Not the Other Options:

Classification: Used for predicting discrete categories or labels (e.g., “on-time” vs. “late”). It does not output continuous numbers.

Clustering: An unsupervised learning technique used to group data points with similar characteristics, not to make numeric predictions.

Thus, when the output variable is a numeric prediction (such as hours, prices, quantities, or time), the correct machine learning task is Regression.

In the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning (AML) learning paths, deploying a real-time inference pipeline refers to making a trained machine learning model available as a web service that can process incoming data and return predictions instantly. To achieve this, the model must be deployed to an infrastructure capable of handling continuous, low-latency requests with high reliability and scalability.

Microsoft’s official guidance from Azure Machine Learning documentation specifies that:

For testing or development, you can deploy to Azure Container Instances (ACI) because it provides a lightweight, temporary environment suitable for small-scale or non-production workloads.

For production-grade, real-time inference, the deployment should be made to Azure Kubernetes Service (AKS).

AKS provides enterprise-level scalability, load balancing, and high availability, which are critical for serving real-time predictions to multiple consumers simultaneously. It manages containerized applications using Kubernetes orchestration, allowing the model to scale automatically based on traffic demands.

Azure Machine Learning Compute is mainly used for model training and batch inference pipelines, not real-time endpoints. A local web service is typically used only for debugging or offline testing on a developer machine and cannot be shared for external consumption.

Therefore, when deploying a real-time inference pipeline as a service for others to consume, the correct and Microsoft-verified option is Azure Kubernetes Service (AKS). This environment ensures production readiness, secure endpoint management, and scalability for live AI applications, fully aligning with best practices outlined in the Azure Machine Learning designer documentation and AI-900 exam objectives.

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.

Convert spoken requests into text → Azure AI SpeechThe Azure AI Speech service provides speech-to-text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.

Identify the intent of a user’s requests → Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents—the goals or actions a user wants to perform—and entities, which are key details within that request. For example, in the command “Book a flight to Paris,” the intent is “book a flight,” and the entity is “Paris.”

Apply intent to entities and utterances → Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.

In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities—a workflow essential in intelligent conversational applications.

According to the Microsoft Learn module “Explore speech capabilities of Azure AI” and the AI-900 Official Study Guide, speech synthesis (also known as text-to-speech) is the process of converting written text into spoken audio output. Azure’s Speech service provides this functionality, allowing applications to produce human-like voices dynamically.

Let’s evaluate each scenario:

A. Automated voice that reads back a credit card number entered into a telephone keypad → YesThis is a classic text-to-speech (TTS) use case. The application converts numeric or textual input (such as a credit card number) into audio output that the caller hears. Azure Speech service can handle such voice responses in automated phone systems or IVR (Interactive Voice Response) setups.

B. Generating live captions for a news broadcast → NoThis is a speech-to-text scenario (speech recognition), not speech synthesis. It involves converting audio speech into written text.

C. Extracting key phrases from an audio recording of a meeting → NoThis involves speech-to-text followed by text analytics, not speech synthesis.

D. An AI character in a computer game that speaks audibly to a player → YesThis is a direct example of speech synthesis, where the character’s dialog text is converted into realistic spoken output for immersive interaction.

Therefore, based on Microsoft’s AI-900 curriculum, speech synthesis is used in applications that convert text into audible speech, such as automated voice systems or interactive digital characters.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of conversational AI workloads on Azure,” the Azure Bot Service is the dedicated Azure service for building, connecting, deploying, and managing conversational AI experiences across multiple channels — such as Microsoft Teams, Cortana, and Amazon Alexa.

The Azure Bot Service integrates with the Bot Framework SDK to design intelligent chatbots that can communicate with users in natural language. It also connects seamlessly with other Azure Cognitive Services, such as Language Service (LUIS) for intent understanding and Speech Service for voice input/output.

The question specifies that the conversational AI must be accessible through multiple platforms, including Microsoft Teams, Cortana, and Alexa. Azure Bot Service supports this multi-channel communication model out of the box, allowing developers to configure a single bot that interacts through many endpoints simultaneously.

Other options:

B. Azure Cognitive Search: Used for information retrieval and knowledge mining, not conversational AI.

C. Language Service: Provides natural language understanding, key phrase extraction, sentiment analysis, etc., but doesn’t handle multi-channel communication.

D. Speech: Provides speech-to-text and text-to-speech conversion but is not a chatbot platform.

Therefore, the best solution for building and deploying a multi-channel conversational AI system is Azure Bot Service, as clearly defined in Microsoft’s AI-900 learning content.

The correct answers are Yes, Yes, and Yes.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn content in the section “Describe features of conversational AI workloads on Azure”, bots created using Azure Bot Service can interact with users across multiple channels. The AI-900 syllabus explains that Azure Bot Service integrates with various communication platforms, allowing developers to build a single bot that can be deployed in many contexts without rewriting the logic.

“You can communicate with a bot by using Cortana.” – Yes.The AI-900 learning materials explain that Cortana, Microsoft’s intelligent personal assistant, can serve as a channel for bots built with the Azure Bot Service. Through the Bot Framework, bots can be connected to Cortana to allow users to interact via voice or text. Although Cortana is less prominent now, it remains conceptually included in the AI-900 coverage as an example of a voice-based conversational AI channel.

“You can communicate with a bot by using Microsoft Teams.” – Yes.This statement is true and directly referenced in the AI-900 syllabus. Microsoft Teams is a fully supported communication channel for Azure Bot Service. Bots in Teams can handle chat messages, commands, and interactions in team or personal contexts. The Microsoft Learn materials specify Teams as one of the native connectors where enterprise users can interact with organizational bots.

“You can communicate with a bot by using a webchat interface.” – Yes.This is also true. The Web Chat channel is one of the most common ways to deploy bots publicly. Azure Bot Service provides a Web Chat control that can be embedded directly into a webpage or web application. This allows users to interact with the bot using a chat window, just like on customer service websites.

Therefore, all three interfaces—Cortana (voice-based), Microsoft Teams (enterprise chat), and Web Chat (browser-based)—are valid and officially supported communication channels for Azure bots.

The prebuilt receipt model is part of Azure AI Document Intelligence (formerly Form Recognizer), which belongs to the broader Azure AI Services family. The prebuilt receipt model is designed to automatically extract key information such as merchant names, dates, totals, and tax amounts from receipts without requiring custom training.

Among the given options, C. Azure AI Services is correct because it encompasses all cognitive AI capabilities—vision, language, speech, and document processing. Specifically, Azure AI Document Intelligence is included within Azure AI Services and provides both prebuilt and custom models for processing invoices, receipts, business cards, and identity documents.

Options A (Computer Vision) and D (Custom Vision) are image-based services, not form-processing tools. Option B (Azure Machine Learning) focuses on building custom predictive models, not using prebuilt document models.

Therefore, the correct answer is C. Azure AI Services, which includes the prebuilt receipt model in Document Intelligence.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft’s Responsible AI Principles, one of the core principles is “Privacy and Security.” This principle ensures that AI systems protect personal and sensitive information, maintaining compliance with privacy laws, data protection regulations, and ethical data-handling practices.

If a Natural Language Processing (NLP) model is created using data obtained without permission, it directly violates this principle. Data collected without proper consent breaches user privacy and potentially violates regulations such as GDPR (General Data Protection Regulation) or other global privacy frameworks.

The Privacy and Security principle emphasizes the following:

AI systems must ensure data collection and usage transparency.

Data must be lawfully acquired and used with consent.

Systems should protect data against unauthorized access or misuse.

In contrast:

Inclusiveness promotes accessibility and fairness for all users.

Transparency focuses on explaining how AI systems make decisions.

Reliability and safety ensure systems function as intended and minimize harm.

Therefore, using unapproved data clearly breaches Privacy and Security, as it involves unethical data sourcing and endangers user trust.

According to the Microsoft Azure AI Fundamentals (AI-900) official learning path, the appropriate service for recognizing celebrities in images is Azure AI Vision (formerly Computer Vision). This service is part of Azure’s Cognitive Services suite and specializes in analyzing visual content using pretrained deep learning models. One of its built-in capabilities, as documented in Microsoft Learn: “Analyze images with Azure AI Vision”, includes object detection, face detection, and celebrity recognition.

The Azure AI Vision Analyze API can detect and identify thousands of objects, brands, and celebrities. When an image is submitted to the service, the model compares detected faces to a known database of public figures and returns metadata including celebrity names, confidence scores, and bounding box coordinates. This makes it ideal for applications that need to recognize well-known individuals automatically—such as media cataloging, content tagging, or entertainment apps.

The other options are incorrect:

A. Azure OpenAI Service provides generative AI and language models (like GPT-4), but it cannot analyze image content directly in the context of AI-900 fundamentals.

B. Azure Machine Learning is for custom model training and deployment, not a prebuilt vision recognition service.

C. Conversational Language Understanding (CLU) processes natural language input, not images.

Therefore, the correct service for identifying celebrities in images is D. Azure AI Vision.