Courseiva

AI-900 Practice Question: Describe features of computer vision workloads on Azure

What does the 'image analysis' API in Azure AI Vision return when given an image?

⚠ Common exam trap

It's easy for candidates to confuse the Image Analysis API with other Azure services like the Custom Vision API (which requires training) or the Bing Image Search API, leading them to choose options that describe unrelated functionalities.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Rich metadata including captions, detected objects, tags, colour analysis, and content flags

The Image Analysis API in Azure AI Vision returns rich metadata about the image content, including captions, detected objects, tags, color analysis, and content moderation flags. This is because the API applies pre-trained deep learning models to extract semantic information from the image, not raw pixel data or aesthetic scores.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The raw pixel data of the image in a compressed format

    Why it's wrong here

    The Image Analysis service does not return the original byte stream or a re-encoded version of the submitted image. It accepts JPEG, PNG, or other compressed input, then returns a JSON object of semantic metadata derived from those pixels. Raw pixel data would be a lossy or lossless representation of the image itself, conveying no understanding of the scene. The service's output is structured descriptions and detections, not the image.

  • Rich metadata including captions, detected objects, tags, colour analysis, and content flags

    Why this is correct

    Azure AI Vision's Analyze Image API returns precisely this rich metadata: a human-readable caption and tags, detected objects with bounding-box coordinates, dominant foreground/background/accent colors, and moderation scores for adult, racy, or violent content. These outputs come as a structured JSON response that can be consumed directly by applications for accessibility, search indexing, or content governance. This is the intended output of the image-analysis pipeline, which uses pre-trained neural networks to interpret visual features.

  • A score from 1 to 10 rating the aesthetic quality of the photograph

    Why it's wrong here

    The API does not compute an aesthetic or artistic-quality rating; such a score is inherently subjective and is not part of Azure AI Vision's predefined output schema. Image analysis focuses on factual, content-based information, such as whether an object is present or a scene's dominant color. While Azure offers custom vision models for bespoke scoring, the built-in Image Analysis endpoint exposes only enumerated metadata fields, not a 1–10 beauty score.

  • A list of similar images found across the web

    Why it's wrong here

    Matching the submitted image against web-scale collections is the job of a separate search service, such as Bing Visual Search, which maintains an index of images across the internet. The Image Analysis service is explicitly scoped to the single input image; it has no access to a global image repository to find visually similar photos. Its output is derived entirely from the supplied pixel data, not from any reference to other images.

About these practice questions

Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.