AI-900 Practice Questions

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, object detection is a type of computer vision workload that not only identifies objects within an image but also determines their location by drawing bounding boxes around them. This functionality is clearly described in the Microsoft Learn module “Identify features of computer vision workloads.”

In this scenario, the AI system analyzes an image to find a vehicle and then returns a bounding box showing where that vehicle is located within the image frame. That ability — to detect, classify, and localize multiple objects — perfectly defines object detection.

Microsoft’s study content contrasts object detection with other computer vision workloads as follows:

Image classification: Determines what object or scene is present in an image as a whole but does not locate it (e.g., “this is a car”).

Object detection: Identifies what objects are present and where they are, usually returning coordinates for bounding boxes (e.g., “car detected at position X, Y”).

Optical Character Recognition (OCR): Extracts text content from images or scanned documents.

Facial detection: Specifically locates human faces within an image or video feed, often as part of face recognition systems.

In Azure, object detection capabilities are available through services such as Azure Computer Vision, Custom Vision, and Azure Cognitive Services for Vision, which can be trained to detect vehicles, products, or other objects in various image datasets.

Therefore, based on the AI-900 study guide and Microsoft Learn materials, the verified and correct answer is Object detection, as it accurately describes the process of returning a bounding box indicating an object’s position in an image.

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn’s modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, “This is a product”), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.

In the scenario described — identifying the location of products on a conveyor belt — the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.

Let’s evaluate the other options:

Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.

Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.

Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.

Thus, per Microsoft Learn’s AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.

✅ Final Answer: Object detection

The Azure Text Analytics service, a component of Azure Cognitive Services, provides natural language processing (NLP) capabilities to analyze and understand text-based data. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features and uses for Natural Language Processing (NLP)”, the Text Analytics service supports multiple text understanding tasks, such as language detection, key phrase extraction, sentiment analysis, and entity recognition.

Language Identification – Yes:Text Analytics can automatically detect the language in which text is written. This feature analyzes linguistic patterns and assigns a language code (for example, “en” for English, “es” for Spanish). It is one of the primary features described in Microsoft Learn as part of the service’s Language Detection API.

Detect Handwritten Signatures – No:Detecting handwritten signatures is not a text-based NLP task. Instead, it belongs to the computer vision domain, specifically Optical Character Recognition (OCR). The Text Analytics service only processes digital text, not handwritten or image-based data. To detect handwriting or signatures, you would use the Computer Vision OCR API, not Text Analytics.

Entity Recognition – Yes:The Text Analytics service can identify named entities—such as people, locations, organizations, dates, and quantities—within documents. This is known as Named Entity Recognition (NER), which helps extract structured information from unstructured text.

The correct answers are B and

D.  

Speech recognition, part of Azure’s Speech service, converts spoken audio into written text. It is a core feature of Azure Cognitive Services for speech-to-text scenarios.

Providing closed captions for recorded or live videos (B) – This is a typical application of speech recognition. The AI system listens to audio content from a video and generates real-time or post-event captions. Azure’s Speech-to-Text API is frequently used in broadcasting and video platforms to improve accessibility and searchability.

Creating a transcript of a telephone call or meeting (D) – Another common use case is automated transcription. The Speech service can process real-time audio streams (such as meetings or calls) and produce accurate text transcripts. This is widely used in customer service, call analytics, and meeting documentation.

The incorrect options are:

A.  

an in-car system that reads text messages aloud – This uses Text-to-Speech, not speech recognition.

C.  

creating an automated public address system for a train station – This also uses Text-to-Speech, since it generates spoken output from text.

Therefore, scenarios that convert spoken words into text correctly represent speech recognition, making B and D the right answers.

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.

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.