AI-900 Practice Questions

The correct answer is Azure AI Custom Vision.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore computer vision in Azure”, the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.

The Azure AI Custom Vision service extends the capabilities of Azure’s general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios—such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images—where general-purpose models may not suffice.

By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.

The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:

Image Classification – categorizing entire images based on predefined tags.

Object Detection – identifying and locating multiple objects within an image by drawing bounding boxes.

Therefore, when the question specifies “train an object detection model by using your own images,” the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user’s dataset.

Hence, the verified correct answer is: Azure AI Custom Vision.

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.

Generate captions for images → Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn’s “Describe features of computer vision workloads,” image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image’s contents in human-readable language—based on what the model identifies as key visual features.

Extract movie title names from movie poster images → Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision’s Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents. In this case, the movie title text from a poster is best extracted using OCR.

Locate vehicles in images → Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It’s ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.

In Microsoft’s Responsible AI framework, the Reliability and Safety principle ensures that AI systems perform consistently, safely, and as intended across diverse conditions — even when faced with incomplete, unusual, or unexpected data. Correctly handling unusual or missing values in a dataset directly demonstrates this principle, as it helps prevent faulty predictions, biased results, or unsafe system behaviors.

According to the Microsoft Learn Responsible AI module (from the AI-900 and AI-102 study paths), a reliable AI model should maintain its performance when encountering data anomalies. This includes validating inputs, managing missing or extreme values, and testing models to ensure they behave as expected in real-world scenarios. Such practices make AI systems robust and trustworthy, which aligns exactly with the Reliability and Safety principle.

The other Responsible AI principles address different concerns:

Inclusiveness: Ensures AI empowers and serves all users equitably.

Privacy and Security: Focuses on safeguarding personal data and preventing unauthorized access.

Transparency: Ensures that AI decisions are understandable and explainable to users.

While all principles are essential, managing data integrity and system stability—including how a model responds to missing or anomalous values—is primarily a matter of reliability and safety. It ensures the AI behaves predictably and minimizes risks of errors or unintended harm.

Therefore, the correct completion of the sentence is:

“Correctly handling unusual or missing values is an example of the application of the Reliability and Safety principle for Responsible AI.”