Microsoft Updated AI-900 Exam Questions and Answers by jayson

Box 1: Yes

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Box 2: No

Box 3: Yes

During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to " fit " your data. It will stop once it hits the exit criteria defined in the experiment.

Box 4: No

Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.

The label is the column you want to predict.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module “Describe Azure Machine Learning and Automated ML,” Azure Machine Learning designer (formerly known as Azure Machine Learning Studio) is a drag-and-drop, low-code/no-code environment that allows users to create, train, and evaluate machine learning models visually — without the need for extensive programming knowledge.

The designer provides a visual interface, known as the canvas, where users can:

Import and prepare data using modules for data transformation and cleaning.

Split data into training and testing datasets.

Select and configure algorithms (classification, regression, or clustering).

Train and evaluate the model.

Deploy the model as a web service directly from the designer.

The official Microsoft Learn content emphasizes that “Azure Machine Learning designer enables users to build, test, and deploy models by adding and connecting prebuilt modules on a visual interface.” This allows business analysts, data professionals, and beginners to experiment with machine learning workflows without writing code.

By comparison:

Automatically performing common data preparation tasks refers to Automated ML, not the designer.

Automatically selecting an algorithm is also part of Automated ML, which optimizes models algorithmically.

Using a code-first notebook experience applies to Azure Machine Learning notebooks, intended for data scientists familiar with Python and SDKs.

Therefore, as per the AI-900 study guide and Microsoft Learn documentation, the verified and correct answer is:

✅ Adding and connecting modules on a visual canvas, which accurately describes how Azure Machine Learning designer operates.

The correct answers are C. the Azure OpenAI GPT-4 model and D. the GitHub Copilot service.

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn documentation on Azure OpenAI and GitHub Copilot, both GPT-4 and GitHub Copilot can be used to analyze and generate explanations for code functionality, as well as produce or refine code comments.

Azure OpenAI GPT-4 model (C):The GPT-4 model is a large language model (LLM) developed by OpenAI and available through the Azure OpenAI Service. It is trained on vast amounts of text, including programming languages, documentation, and natural language instructions. This enables it to interpret source code, explain what it does, suggest optimizations, and automatically generate detailed code comments. When prompted with code snippets, GPT-4 can provide structured natural language explanations describing the logic and intent of the code. In enterprise scenarios, developers use Azure OpenAI GPT models for code understanding, review automation, and documentation generation.

GitHub Copilot service (D):GitHub Copilot, powered by OpenAI Codex, is an AI coding assistant integrated into IDEs such as Visual Studio Code. It can analyze code context and generate inline comments and explanations in real time. GitHub Copilot understands the syntax and intent of numerous programming languages and provides intelligent suggestions or explanations directly in the developer’s environment.

The other options are not suitable:

A. DALL-E is a generative image model for creating visual content, not text or code analysis.

B. Whisper is an automatic speech recognition (ASR) model used for converting speech to text, unrelated to code interpretation.

Therefore, based on the official Azure AI and GitHub documentation, the correct and verified answers are C. Azure OpenAI GPT-4 model and D. GitHub Copilot service.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, the three main machine learning model types differ by their purpose and the kind of data they use — whether supervised (using labeled data) or unsupervised (using unlabeled data).

Regression → A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for “predicting numeric values based on known data,” using algorithms such as linear regression or decision tree regression.

Classification → A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values. It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as “predicting a category or label for new observations.”

Clustering → An unsupervised machine learning model used to group similar entities based on features.Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as “discovering natural groupings in data without predefined labels.”

Thus, per Microsoft’s official AI-900 learning objectives:

Regression → Predicts numeric/continuous values.

Classification → Predicts categories/labels.

Clustering → Groups similar entities (unsupervised).

✅ Final Verified Configuration:

Regression → Predict numeric values

Classification → Predict categories

Clustering → Group similar entities based on features