AI-900 Practice Questions

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.

The correct matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure.” These materials explain that facial recognition tasks can be categorized into four major operations: verification, identification, similarity, and grouping. Each task serves a distinct purpose in facial recognition scenarios.

Verification – “Do two images of a face belong to the same person?”The verification task determines whether two facial images represent the same individual. Azure Face API compares the facial features and returns a confidence score indicating the likelihood that the two faces belong to the same person.

Similarity – “Does this person look like other people?”The similarity task compares a face against a collection of faces to find visually similar individuals. It does not confirm identity but measures how closely two or more faces resemble each other.

Grouping – “Do all the faces belong together?”Grouping organizes a set of unknown faces into clusters based on similar facial features. This is used when identities are not known beforehand, helping discover potential duplicates or visually similar clusters within an image dataset.

Identification – “Who is this person in this group of people?”The identification task is used when the system tries to determine who a specific person is by comparing their face against a known collection (face database or gallery). It returns the identity that best matches the input face.

According to Microsoft’s AI-900 training, these tasks form the basis of Azure Face API’s capabilities. Each helps solve a different type of facial recognition problem—from matching pairs to discovering unknown identities—making them essential components of responsible AI-based vision systems.

The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn’s module “Translate text with the Translator service”, this service provides two main capabilities: text translation and automatic language detection.

“You can use the Translator service to translate text between languages.” → YesThis statement is true. The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.

“You can use the Translator service to detect the language of a given text.” → YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.

“You can use the Translator service to transcribe audible speech into text.” → NoThis statement is false. Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech-to-Text API, not the Translator service.

Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.