Microsoft AI-900 - Microsoft Azure AI Fundamentals

Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.

The correct answers are A and D because both scenarios involve extracting structured information from documents, which is exactly what Azure Form Recognizer is designed to do.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore computer vision”, Form Recognizer is an Azure Cognitive Service that uses advanced Optical Character Recognition (OCR) and machine learning to extract key-value pairs, tables, and text from structured and semi-structured documents such as receipts, invoices, business cards, and forms. It allows organizations to automate data entry and digitize document processing.

A. Extract the invoice number from an invoice → CorrectForm Recognizer can identify fields such as invoice number, total amount, date, vendor name, and billing address directly from invoices. It uses prebuilt models for invoices and receipts that automatically detect and extract relevant information without requiring extensive manual labeling. As stated in Microsoft Learn, “Form Recognizer extracts information from documents like receipts and invoices and returns structured data including key-value pairs.”

D. Identify the retailer from a receipt → CorrectThe prebuilt receipt model in Form Recognizer can read printed or scanned receipts and extract data points such as retailer name, transaction date, total amount, and tax information. This makes it ideal for expense reporting, auditing, or financial reconciliation.

The following options are incorrect:

B. Translate a form from French to English → This task involves language translation, which is performed by Azure Translator, not Form Recognizer.

C. Find an image of a product in a catalog → This requires object detection or image classification, which are part of Computer Vision, not Form Recognizer.

Therefore, based on Microsoft’s AI-900 learning objectives and documentation, the two correct scenarios are:

✅ A. Extract the invoice number from an invoice

✅ D. Identify the retailer from a receipt

According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Azure Text Analytics service documentation, key phrase extraction is a natural language processing (NLP) technique used to automatically identify the main topics or talking points within a text document or a collection of documents. This feature is designed to summarize textual data by detecting the most relevant words or short phrases that capture the essence of the content.

For example, if a document discusses “renewable energy sources such as solar and wind power,” the key phrases extracted might include “renewable energy,” “solar power,” and “wind power.” This helps users quickly understand the primary focus areas of large volumes of text without manual review.

In Azure, the Text Analytics service provides several core NLP capabilities, including:

Key phrase extraction – identifies main concepts or topics.

Entity recognition – detects and categorizes proper names like people, locations, or organizations.

Sentiment analysis – determines the emotional tone (positive, neutral, or negative).

Language detection – identifies the language used in the text.

Since the question specifies identifying main talking points, the correct feature is key phrase extraction, as it focuses on summarizing themes rather than identifying entities or emotions.

Therefore, the verified answer is B. key phrase extraction.

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.

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, language modeling is described as a core component of Natural Language Processing (NLP) that enables an AI system to understand, interpret, and generate human language in context. The AI-900 Microsoft Learn module “Identify features of Natural Language Processing workloads” explains that language modeling is used to analyze user input and determine intent — essential for conversational systems like chatbots.

In this question, the chatbot must:

Accept customer orders.

Retrieve support documents.

Retrieve order status updates.

All these tasks require the bot to understand user intent and context from text input. This understanding process is driven by language modeling, which predicts meaning and structure within sentences, enabling the system to decide what action to take next.

Microsoft Learn distinguishes between various NLP techniques:

Sentiment analysis detects emotional tone (positive/negative/neutral).

Translation converts text between languages.

Named Entity Recognition (NER) identifies specific entities like names or dates.However, none of these individually allow a system to process commands, requests, or user intents — that capability is part of language modeling, which powers LUIS (Language Understanding Intelligent Service) or the modern Azure Cognitive Service for Language.

Therefore, to build a chatbot that can interpret commands and respond contextually — such as processing orders or retrieving documents — you must use language modeling.

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Azure OpenAI documentation, the appropriate service for detecting and managing harmful, unsafe, or inappropriate content in text, images, or other generative AI outputs is Azure AI Content Safety.

Azure AI Content Safety is designed to automatically detect potentially harmful material such as hate speech, violence, self-harm, sexual content, or profanity. It ensures that generative AI applications like chatbots, image generators, and content creation tools comply with Microsoft’s Responsible AI principles — specifically Reliability & Safety and Accountability.

This service integrates directly with the Azure OpenAI Service, meaning that when developers build AI solutions using models like GPT-4 or DALL·E, they can use Content Safety to filter and moderate both input prompts and model outputs. This protects users from unsafe or offensive content generation.

Let’s analyze why the other options are incorrect:

A. Face – The Face service detects and analyzes human faces in images or videos. It is unrelated to moderating harmful textual or generative content.

B. Video Analysis – This service analyzes video streams to detect objects, actions, or events but not inappropriate or harmful text or imagery from AI models.

C. Language – The Azure AI Language service focuses on text understanding tasks like sentiment analysis, entity recognition, and translation, not content safety filtering.

Therefore, per Microsoft Learn’s official AI-900 guidance, when identifying or filtering harmful content in a generative AI solution built with Azure OpenAI, the correct and verified service to use is Azure AI Content Safety.

Box 1: Entity recognition

the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.

Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:

Which companies were mentioned in a news article?

Does a tweet contain the name of a person? Does the tweet also provide his current location?

Were specified products mentioned in complaints or reviews?

Box 2: Sentiment Analysis

The Text Analytics API ' s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as " negative " , " neutral " and " positive " ) and confidence scores at the sentence and document-level.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of common machine learning types”, the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.

In a supervised learning process, data is typically divided into two parts:

Features → The input variables used by the model to learn patterns (e.g., customer age, income, credit score).

Label (Target) → The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).

During training, the model uses the features to understand how input data correlates with the target output. Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.

For example:

In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.

In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.

To contrast with other options:

Dependent variables (or labels) are the outcomes the model predicts.

Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.

Labels are the target outputs, not the inputs.

Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called “features.”

This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section “Describe features of natural language processing (NLP) workloads on Azure.” According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent.

Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given—classifying emails as “work-related” or “personal”—is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context.

Let’s analyze the other options:

Predict the number of future car rentals → This is a forecasting task, handled by machine learning regression models, not NLP.

Predict which website visitors will make a transaction → This is a classification or prediction problem in machine learning, not NLP, since it deals with behavioral or numerical data rather than language.

Stop a process in a factory when extremely high temperatures are registered → This is an IoT or anomaly detection scenario, focusing on sensor data, not language understanding.

Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case. It illustrates how NLP can analyze written text and make intelligent categorizations—a key capability covered in AI-900’s natural language workloads section.