AI-900 Practice Questions

The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under “Identify features of Azure Machine Learning”. Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle — from creating workspaces, to training models, to automating experimentation.

The Azure portal → Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region — tasks that are part of the setup stage rather than model development.

Machine Learning designer → Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly “Azure ML Studio (classic)”) provides a visual, no-code/low-code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.

Automated machine learning (Automated ML) → Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.

Together, these tools streamline the machine learning workflow:

Azure portal for setup and resource management,

Machine Learning designer for visual model creation, and

Automated ML for guided, automated model selection and tuning.

 “Generative AI enables software applications to generate new content, such as language dialogs and images.” — YES

This statement is true. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, Generative AI refers to systems capable of creating new content such as text, audio, images, video, and code. Models like GPT, DALL·E, and Codex use deep learning to generate human-like responses, natural conversations, or creative media. This is a key differentiator between generative and discriminative AI — generative AI produces new data, while discriminative AI categorizes or analyzes existing data.

 “The difference between a large language model (LLM) and a small language model (SLM) is the number of variables in the model.” — YES

This statement is true. The primary distinction between an LLM and an SLM lies in the scale of parameters (variables) within the neural network. LLMs contain billions or even trillions of parameters, which enable them to capture complex linguistic patterns and perform broader tasks. SLMs have fewer parameters, making them faster but less capable of handling complex, context-rich tasks.

 “Generative AI is a type of supervised learning.” — NO

This statement is false. Generative AI models are typically trained using unsupervised or self-supervised learning methods. They learn by predicting missing or next elements in large text or image datasets rather than relying on labeled input-output pairs, which are used in supervised learning.

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 answer is “adding and connecting modules on a visual canvas.”

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore automated machine learning in Azure Machine Learning,” the Azure Machine Learning designer is a drag-and-drop, no-code environment that allows users to create, train, and deploy machine learning models visually. It is specifically designed for users who prefer an intuitive graphical interface rather than writing extensive code.

Microsoft Learn defines Azure Machine Learning designer as a tool that allows you to “build, test, and deploy machine learning models by dragging and connecting pre-built modules on a visual canvas.” These modules can represent data inputs, transformations, training algorithms, and evaluation processes. By linking them together, users can create an end-to-end machine learning pipeline.

The designer simplifies the machine learning workflow by allowing data scientists, analysts, and even non-developers to:

Import and prepare datasets visually.

Choose and connect algorithm modules (e.g., classification, regression, clustering).

Train and evaluate models interactively.

Publish inference pipelines as web services for prediction.

Let’s analyze the other options:

Automatically performing common data preparation tasks – This describes Automated ML (AutoML), not the Designer.

Automatically selecting an algorithm to build the most accurate model – Also a characteristic of AutoML, where the system tests multiple algorithms automatically.

Using a code-first notebook experience – This describes the Azure Machine Learning notebooks environment, which uses Python and SDKs, not the Designer interface.

Therefore, based on the official AI-900 learning objectives and Microsoft Learn documentation, the Azure Machine Learning designer allows you to create models by adding and connecting modules on a visual canvas, providing a no-code, interactive experience ideal for users building custom machine learning workflows visually.

This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore natural language processing.” NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language — both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.

Monitoring online service reviews for profanities → YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: “Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content.” Hence, this task is correctly classified as NLP.

Identifying brand logos in an image → NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.

Monitoring public news sites for negative mentions of a product → YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.