Practice Exam Questions

---

Question 1

A company wants to automatically generate short descriptions such as “A group of people standing on a beach” for images uploaded to its website. No model training is required.

Which Azure service should be used?

A. Azure Machine Learning
B. Azure AI Vision image analysis
C. Azure Custom Vision
D. Azure OpenAI Service

Correct Answer: B

Explanation:
Azure AI Vision image analysis can generate natural language descriptions of images using prebuilt models. Azure Machine Learning and Custom Vision require training, and Azure OpenAI is not designed for image analysis tasks.

---

Question 2

Which Azure AI Vision capability extracts printed and handwritten text from scanned documents and images?

A. Image tagging
B. Object detection
C. Optical Character Recognition (OCR)
D. Facial analysis

Correct Answer: C

Explanation:
OCR is specifically designed to detect and extract text from images, including scanned documents and handwritten content.

---

Question 3

A developer needs to identify objects in an image and return their locations using bounding boxes.

Which Azure AI Vision feature should be used?

A. Image classification
B. Image tagging
C. Object detection
D. Image description

Correct Answer: C

Explanation:
Object detection identifies what objects are present and where they are located using bounding boxes and confidence scores.

---

Question 4

Which capability of Azure AI Vision can detect faces and return attributes such as estimated age and facial expression?

A. Facial recognition
B. Facial detection and facial analysis
C. Image classification
D. Custom Vision

Correct Answer: B

Explanation:
Azure AI Vision supports facial detection and analysis, which provides facial attributes but does not identify individuals.

---

Question 5

A solution must automatically assign keywords like “outdoor”, “food”, or “animal” to images for search and organization.

Which Azure AI Vision feature meets this requirement?

A. OCR
B. Object detection
C. Image tagging
D. Facial analysis

Correct Answer: C

Explanation:
Image tagging assigns descriptive labels to images to improve categorization and searchability.

---

Question 6

Which statement best describes Azure AI Vision?

A. It requires training a custom model for each scenario
B. It provides prebuilt computer vision capabilities through APIs
C. It is only used for facial recognition
D. It can only analyze video streams

Correct Answer: B

Explanation:
Azure AI Vision offers prebuilt computer vision models accessed via APIs, requiring no model training.

---

Question 7

A company wants to analyze images quickly without building or training a machine learning model.

Which Azure service is most appropriate?

A. Azure Machine Learning
B. Azure Custom Vision
C. Azure AI Vision
D. Azure Databricks

Correct Answer: C

Explanation:
Azure AI Vision is designed for quick deployment using prebuilt models, making it ideal when no custom training is required.

---

Question 8

Which task is NOT a capability of Azure AI Vision?

A. Detecting objects in an image
B. Extracting text from images
C. Identifying specific individuals in photos
D. Generating image descriptions

Correct Answer: C

Explanation:
Azure AI Vision does not identify individuals. Facial recognition and identity verification are restricted and not required for AI-900.

---

Question 9

A scenario mentions analyzing images while following Microsoft’s Responsible AI principles, particularly around privacy and fairness.

Which Azure AI Vision feature is most closely associated with these considerations?

A. Image tagging
B. Facial detection and analysis
C. OCR
D. Object detection

Correct Answer: B

Explanation:
Facial detection and analysis involve human data and are closely tied to privacy, fairness, and transparency considerations.

---

Question 10

When should Azure AI Vision be used instead of Azure Custom Vision?

A. When you need a highly specialized image classification model
B. When you want full control over training data
C. When you need prebuilt image analysis without training
D. When labeling thousands of custom images

Correct Answer: C

Explanation:
Azure AI Vision is ideal for prebuilt, general-purpose image analysis scenarios. Custom Vision is used when custom training is required.

---

Final Exam Tips for This Topic

• Think prebuilt vs custom
• Azure AI Vision = no training
• OCR = text extraction
• Object detection = what + where
• Facial analysis ≠ facial recognition

---

Go to the AI-900 Exam Prep Hub main page.

Overview

Azure AI Vision is Microsoft’s prebuilt computer vision service that enables applications to analyze images and videos without requiring machine learning expertise or custom model training. It provides REST APIs and SDKs that allow developers to easily extract visual insights such as objects, text, faces, and image descriptions.

For the AI-900 exam, you are expected to understand what Azure AI Vision can do, which problems it solves, and how it differs from custom vision solutions—not how to build or tune models.

---

What Is Azure AI Vision?

Azure AI Vision is part of Azure AI Services and offers ready-to-use computer vision capabilities, including:

• Image analysis
• Optical Character Recognition (OCR)
• Facial detection and analysis
• Object detection
• Image tagging and categorization

These capabilities are powered by Microsoft-trained deep learning models and are accessed via APIs.

---

Core Capabilities of Azure AI Vision

1. Image Analysis

Azure AI Vision can analyze images to extract high-level insights, such as:

• Objects present in an image (for example, car, building, person)
• Scene descriptions in natural language
• Image tags and categories
• Visual features such as color distribution

Example use cases:

• Auto-generating image captions
• Content moderation
• Organizing image libraries

👉 Exam tip: Image analysis describes what is in an image, not where every object is located with precision.

---

2. Object Detection

Object detection identifies specific objects in an image and returns:

• Object names
• Bounding box coordinates
• Confidence scores

Example use cases:

• Detecting vehicles in traffic images
• Identifying products on store shelves

👉 Exam tip: Object detection includes location + object type, unlike image classification which only labels the image as a whole.

---

3. Optical Character Recognition (OCR)

OCR extracts printed and handwritten text from images and documents.

Azure AI Vision OCR supports:

• Multiple languages
• Structured and unstructured text
• Images, screenshots, and scanned documents

Example use cases:

• Digitizing receipts
• Reading license plates
• Extracting text from scanned forms

👉 Exam tip: OCR is about reading text, not understanding its meaning.

---

4. Facial Detection and Facial Analysis

Azure AI Vision can detect human faces in images and analyze non-identifying facial attributes, such as:

• Face location (bounding boxes)
• Facial landmarks
• Estimated age range
• Facial expressions
• Accessories (glasses, masks)

⚠️ It does NOT identify individuals.

Example use cases:

• Blurring faces for privacy
• Counting people in images
• Analyzing expressions in photos

👉 Exam tip:

• Facial detection = where faces are
• Facial analysis = attributes of faces
• Facial recognition = identity (not required for AI-900)

---

5. Image Tagging and Categorization

Azure AI Vision automatically assigns tags and categories to images, such as:

• “outdoor”
• “food”
• “animal”

These tags help with searchability and organization.

Example use cases:

• Image indexing
• Content filtering
• Metadata enrichment

👉 Exam tip: Tagging helps describe images at a high level, not detect precise objects.

---

Azure AI Vision vs Custom Vision

Feature	Azure AI Vision	Azure Custom Vision
Prebuilt models	✅ Yes	❌ No
Requires training	❌ No	✅ Yes
Quick setup	✅ Yes	❌ No
Specialized scenarios	❌ Limited	✅ Strong
AI-900 focus	✅ Yes	⚠️ Limited

👉 Exam takeaway:
If the question mentions no training, quick setup, or prebuilt models, Azure AI Vision is usually the right answer.

---

Responsible AI Considerations

Because Azure AI Vision can analyze images of people, Microsoft emphasizes:

• Privacy and security of image data
• Transparency in how visual data is processed
• Fairness and bias mitigation
• Appropriate use of facial analysis

👉 Exam tip: Facial capabilities often pair with Responsible AI principles in exam questions.

---

Common AI-900 Exam Scenarios

You should recognize Azure AI Vision when the scenario involves:

• Analyzing images without training a model
• Extracting text from images
• Detecting faces but not identifying people
• Automatically tagging or describing images

---

Key Exam Takeaways

• Azure AI Vision is a prebuilt computer vision service
• No machine learning expertise required
• Supports image analysis, OCR, object detection, and facial analysis
• Focuses on insight extraction, not identity
• Frequently tested in scenario-based questions

---

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.