Microsoft AI-900 - Microsoft Azure AI Fundamentals

The described app identifies the coordinates of a product within an image of a supermarket shelf. This scenario directly corresponds to the object detection capability of Azure AI Custom Vision. As per the Microsoft Learn module “Train a Custom Vision model”, the object detection project type allows developers to train models that can both detect and locate objects within an image. It returns bounding box coordinates along with predicted labels for each detected item.

In this use case, the app doesn’t just classify what products are present—it needs the position of the product (coordinates). That function distinguishes object detection from classification. Classification simply assigns a label to the entire image, while object detection provides spatial information for multiple items in one image.

The other options are incorrect:

B. Azure AI Computer Vision Read and C. Azure AI Computer Vision OCR are used for extracting text from images, not locating objects.

D. Azure AI Custom Vision classification only categorizes images but cannot determine where objects appear.

Therefore, to build an app that finds and locates products in images, the correct choice is A. Azure AI Custom Vision object detection.

What is a Predicted vs. True chart?

Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

<  A webchat bot can interact with users visiting a website → Yes

 Automatically generating captions for pre-recorded videos is an example of conversational AI → No

 A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of conversational AI → Yes

\ These answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure.”

1. A webchat bot can interact with users visiting a website → Yes

This statement is true. A webchat bot is a key example of conversational AI, which allows users to communicate with an intelligent system through natural language. The Azure Bot Service supports a webchat channel, enabling website visitors to ask questions or get assistance directly through a chat interface embedded on a webpage. This allows businesses to provide 24/7 automated support and interactive engagement without human intervention.

2. Automatically generating captions for pre-recorded videos is an example of conversational AI → No

This is incorrect because automatically generating captions involves speech-to-text transcription, which falls under speech recognition and not conversational AI. While it uses AI to convert audio into text, it does not involve interactive communication or natural language dialogue. This task would be handled by Azure AI’s Speech service, not the conversational AI framework.

3. A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of conversational AI → Yes

This is true. Smart assistants like those found in home devices (e.g., voice-activated systems) use conversational AI technologies to process spoken language (using natural language processing and speech recognition) and generate appropriate responses. This interaction represents a classic example of conversational AI, as it allows human-like dialogue between a user and an AI system.

✅ Final Answers:

Webchat bot interacting with users → Yes

Auto-captioning videos → No

Smart home device answering questions → Yes

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of Natural Language Processing (NLP) workloads on Azure”, Azure Cognitive Services provides several text analytics and language understanding workloads that perform different types of language processing tasks. Each workload extracts specific information or performs distinct analysis operations on text data.

Entity Recognition → Extracts persons, locations, and organizations from the textEntity recognition is a feature of Azure Cognitive Service for Language (formerly Text Analytics). It identifies and categorizes named entities in unstructured text, such as people, organizations, locations, dates, and more. The study guide defines this workload as: “Entity recognition locates and classifies named entities in text into predefined categories.” This allows applications to extract structured information from raw text data—for example, identifying “Microsoft” as an organization and “Seattle” as a location.

Sentiment Analysis → Evaluates text along a positive–negative scaleSentiment analysis determines the emotional tone or opinion expressed in a piece of text. It classifies text as positive, negative, neutral, or mixed, which is widely used for social media monitoring, customer feedback, and product reviews. Microsoft’s official documentation describes it as: “Sentiment analysis evaluates text and returns a sentiment score indicating whether the sentiment is positive, negative, neutral, or mixed.”

Translation → Returns text translated to the specified target languageThe Translator service, part of Azure Cognitive Services, automatically translates text from one language to another. It supports multiple languages and provides near real-time translation. The AI-900 content specifies that “translation workloads are used to automatically translate text between languages using machine translation models.”

In summary:

Entity Recognition → Extracts entities like names and locations.

Sentiment Analysis → Determines emotional tone.

Translation → Converts text between languages.

✅ Final Answers:

Extracts persons, locations, and organizations → Entity recognition

Evaluates text along a positive–negative scale → Sentiment analysis

Returns text translated to the specified target language → Translation

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore computer vision”, Optical Character Recognition (OCR) is a form of computer vision that enables a system to detect and extract printed or handwritten text from images or documents. OCR is particularly useful in scenarios where the goal is to digitize textual information from physical documents, such as receipts, invoices, or travel expense forms — exactly as described in this question.

In the given scenario, employees need a mobile application that allows them to scan and store expenses while traveling. The process involves taking photos of receipts that contain printed text, such as vendor names, totals, dates, and item descriptions. The OCR technology automatically detects the text areas within the image and converts them into machine-readable and searchable data that can be stored in a database or processed further for expense management.

Microsoft’s Azure Cognitive Services include the Computer Vision API and the Form Recognizer service, both of which use OCR technology. The Form Recognizer builds upon OCR by adding intelligent document understanding, enabling it to extract structured data from expense receipts automatically.

Other answer options are incorrect for the following reasons:

A. Semantic segmentation assigns labels to every pixel in an image, typically used in autonomous driving or medical imaging, not for text extraction.

B. Image classification identifies the overall category of an image (e.g., “This is a receipt”), but it does not extract the textual content.

C. Object detection identifies and locates objects in an image with bounding boxes but is not used for text reading or conversion.

Therefore, based on the official AI-900 training and Microsoft Learn content, the correct answer is D. Optical Character Recognition (OCR) — the technology that enables extracting textual information from scanned expense receipts.

Azure AI Form Recognizer extracts information from structured and semi-structured documents. A custom model in Form Recognizer allows an organization to train the system on its specific document layouts and data fields.

As per the AI-900 study guide, a key advantage of a custom model is its flexibility. It can be trained with a set of labeled examples (e.g., invoices, purchase orders, receipts) that match the company’s format. Once trained, the model learns where to locate and extract fields such as invoice numbers, dates, or totals—regardless of layout differences between form types.

Option B is correct because a custom model can be trained to recognize a variety of form types, making it adaptable for diverse business processes.

Options A, C, and D are incorrect:

A: Both prebuilt and custom models are cloud-based; on-premises deployment is not an exclusive feature.

C: Custom models are not cheaper; they may involve additional training costs.

D: Custom models do not always guarantee higher accuracy—accuracy depends on the training data quality.

In Azure Machine Learning designer, the Split Data module is specifically designed to divide a dataset into training and validation (or testing) subsets. The AI-900 study guide and the Microsoft Learn module “Split data for training and evaluation” explain that this module allows users to control how data is partitioned, ensuring that models are trained on one portion of the data and tested on unseen data to assess performance.

By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70–80% of the data is used for training and the remaining 20–30% for validation or testing. This ensures the model’s generalizability and prevents overfitting.

The other options serve different purposes:

A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.

B. Add Rows: Combines multiple datasets vertically.

D. Join Data: Combines datasets horizontally based on a common key.

Only Split Data performs the function of dividing data into training and validation subsets.

The correct answer is Document Intelligence.

According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft Learn documentation, the Azure AI Document Intelligence service (formerly known as Form Recognizer) is specifically designed to extract structured data from documents, including scanned invoices, receipts, forms, and business cards.

This service combines optical character recognition (OCR) with machine learning to analyze both the layout and semantic meaning of document content. When processing scanned invoices, Document Intelligence identifies and extracts fields such as invoice numbers, dates, totals, taxes, vendor names, and line-item details. The extracted information can then be automatically imported into business systems like accounting software or databases, eliminating manual data entry and improving operational efficiency.

Here’s why the other options are incorrect:

Generative AI: Focuses on creating new content such as text, images, or code (for example, using GPT-4 or DALL·E). It is not used for structured data extraction.

Natural Language Processing (NLP): Deals with understanding and generating human language from text-based input, not document scanning or layout interpretation.

The Document Intelligence workload excels at handling semi-structured documents where the location and format of data vary between samples. Microsoft’s prebuilt models—like Invoice, Receipt, Identity Document, and Contract—simplify extraction without requiring custom training.

In summary, if the task involves extracting data from scanned invoices, the appropriate Azure AI service is Azure AI Document Intelligence, which uses AI-powered document understanding to convert unstructured document images into structured, usable data.

In Azure Machine Learning designer, a low-code drag-and-drop interface, users can visually build machine learning workflows. According to the AI-900 study guide and Microsoft Learn module “Create and publish models with Azure Machine Learning designer”, two key components that can be dragged onto the designer canvas are datasets and modules.

Datasets (A): These are collections of data that serve as the input for training or evaluating models. They can be registered in the workspace and then dragged onto the canvas for use in transformations or model training.

Modules (D): These are prebuilt processing and modeling components that perform operations such as data cleaning, feature engineering, model training, and evaluation. Examples include “Split Data,” “Train Model,” and “Evaluate Model.”

Compute (B) and Pipeline (C) are not drag-and-drop items within the designer. Compute targets are infrastructure resources used to run the pipeline, while a pipeline represents the overall workflow, not a component that can be added like a dataset or module.

Hence, the correct answers are A. Dataset and D. Module.

A transformer model is the foundational architecture behind many modern natural language processing systems such as GPT and BERT. It processes text data through multiple key stages. According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn materials, the major stages of a transformer-based large language model are tokenization, embedding calculation, and next token prediction.

Tokenization (C) – The first step converts raw text into smaller units called tokens (words, subwords, or characters). This process allows the model to handle text in a structured numerical form rather than as raw language.

Embedding Calculation (B) – After tokenization, the tokens are mapped into high-dimensional numeric vectors, known as embeddings. These embeddings capture semantic relationships between words and phrases so that the model can understand context and meaning.

Next Token Prediction (D) – This stage is the heart of transformer operation, where the model predicts the next likely token in a sequence based on prior tokens. Repeated next-token predictions enable text generation, summarization, or translation.

Options A (object detection) and E (anonymization) are incorrect because they relate to vision and privacy workflows, not language modeling.