Practice Exam Questions

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Question 1

A city wants to analyze traffic camera images to identify and count cars and bicycles. The solution must determine where each vehicle appears in the image. Which computer vision solution should be used?

A. Image classification
B. Image segmentation
C. Object detection
D. Facial recognition

Correct Answer: C

Explanation:
Object detection identifies objects and their locations using bounding boxes, making it ideal for counting and tracking vehicles.

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Question 2

Which output is characteristic of an object detection solution?

A. A single label for the entire image
B. Bounding boxes with labels and confidence scores
C. Pixel-level classification masks
D. Text extracted from images

Correct Answer: B

Explanation:
Object detection returns bounding boxes for detected objects, along with labels and confidence scores.

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Question 3

Which scenario best fits object detection rather than image classification?

A. Tagging photos as indoor or outdoor
B. Determining if an image contains a dog
C. Identifying the locations of multiple people in an image
D. Categorizing images by color theme

Correct Answer: C

Explanation:
Object detection is required when identifying and locating multiple objects within an image.

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Question 4

Which Azure service provides prebuilt object detection models without requiring custom training?

A. Azure Machine Learning
B. Azure AI Custom Vision
C. Azure AI Vision
D. Azure Cognitive Search

Correct Answer: C

Explanation:
Azure AI Vision offers prebuilt computer vision models, including object detection, that require no training.

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Question 5

What is the main difference between object detection and image segmentation?

A. Object detection identifies pixel-level boundaries
B. Image segmentation uses bounding boxes
C. Object detection locates objects using bounding boxes
D. Image segmentation does not use machine learning

Correct Answer: C

Explanation:
Object detection locates objects using bounding boxes, while segmentation classifies each pixel in the image.

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Question 6

Which requirement would make object detection the most appropriate solution?

A. Classifying images into predefined categories
B. Identifying precise pixel boundaries of objects
C. Locating and counting multiple objects in an image
D. Detecting sentiment in text

Correct Answer: C

Explanation:
Object detection is best when both identification and location of objects are required.

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Question 7

A team needs to detect custom manufacturing defects in images of products. Which Azure service should they use?

A. Azure AI Vision (prebuilt models)
B. Azure AI Custom Vision with object detection
C. Azure OpenAI
D. Azure Text Analytics

Correct Answer: B

Explanation:
Azure AI Custom Vision allows training custom object detection models using labeled images with bounding boxes.

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Question 8

Which phrase in an exam question most strongly indicates an object detection solution?

A. “Assign a label to the image”
B. “Extract text from the image”
C. “Identify and locate objects”
D. “Classify image sentiment”

Correct Answer: C

Explanation:
Keywords such as identify, locate, and bounding box clearly point to object detection.

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Question 9

An object detection model returns a confidence score for each detected object. What does this score represent?

A. The size of the object
B. The number of objects detected
C. The model’s certainty in the prediction
D. The training accuracy of the model

Correct Answer: C

Explanation:
Confidence scores indicate how certain the model is about each detected object.

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Question 10

Which statement correctly describes object detection solutions on Azure?

A. They only support single-object images
B. They cannot be used in real-time scenarios
C. They return labels and bounding boxes
D. They do not use machine learning models

Correct Answer: C

Explanation:
Object detection solutions return both object labels and bounding boxes and support real-time and batch scenarios.

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Final AI-900 Exam Pointers 🎯

• Object detection = what + where
• Look for counting, locating, bounding boxes
• Azure AI Vision = prebuilt detection
• Azure AI Custom Vision = custom detection models

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

Overview

Object detection is a key computer vision workload tested on the AI-900 exam. It goes beyond identifying what appears in an image by also determining where those objects are located. Object detection solutions analyze images (or video frames) and return labels, bounding boxes, and confidence scores.

On the AI-900 exam, you must be able to:

• Recognize object detection scenarios
• Distinguish object detection from image classification and image segmentation
• Identify Azure services that support object detection

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What Is Object Detection?

Object detection is a computer vision technique that:

• Identifies multiple objects in an image
• Assigns labels to each object
• Returns bounding boxes showing object locations

It answers the question:

“What objects are in this image, and where are they?”

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Key Characteristics of Object Detection

1. Bounding Boxes

• Objects are located using rectangular boxes
• Each bounding box defines:
  • Position (x, y coordinates)
  • Size (width and height)

This is the clearest differentiator from image classification.

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2. Multiple Objects per Image

Object detection can:

• Detect multiple objects
• Identify different object types in the same image

Example:

• Person
• Bicycle
• Car

Each with its own bounding box.

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3. Labels with Confidence Scores

For each detected object, the solution returns:

• A label (for example, Car)
• A confidence score indicating prediction certainty

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4. Real-Time and Batch Use

Object detection can be used for:

• Real-time scenarios (video feeds, camera streams)
• Batch processing (analyzing stored images)

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Common Object Detection Scenarios

Object detection is appropriate when location matters.

Typical Use Cases

• Counting people or vehicles
• Security and surveillance
• Retail analytics (products on shelves)
• Traffic monitoring
• Autonomous systems (identifying obstacles)

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Object Detection vs Image Classification

Understanding this difference is critical for AI-900.

Feature	Image Classification	Object Detection
Labels entire image	✅	❌
Identifies object locations	❌	✅
Uses bounding boxes	❌	✅
Detects multiple objects	❌	✅

Exam Tip:
If a question mentions “count,” “locate,” “draw boxes,” or “find all”, object detection is the correct choice.

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Azure Services for Object Detection

Azure AI Vision (Prebuilt Models)

• Provides ready-to-use object detection
• Detects common objects
• No training required
• Accessible via REST APIs

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Azure AI Custom Vision

• Supports custom object detection models
• Requires:
  • Labeled images
  • Bounding box annotations
• Ideal for domain-specific objects

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Features of Object Detection Solutions on Azure

Cloud-Based Inference

• Runs in Azure
• Scales automatically
• Accessible via APIs

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Custom vs Prebuilt Models

• Prebuilt models for general use
• Custom models for specialized scenarios

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Integration with Applications

• Can be embedded into:
  • Web apps
  • Mobile apps
  • IoT solutions
• Often used with camera feeds or uploaded images

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When to Use Object Detection

Use object detection when:

• You need to find and locate objects
• Multiple objects may exist
• You need counts or spatial awareness

When Not to Use It

• When only overall image labels are required
• When pixel-level accuracy is needed (segmentation)

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Responsible AI Considerations

At a high level, AI-900 expects awareness of:

• Bias in training images
• Privacy when detecting people
• Transparency in how results are used

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Key Exam Takeaways

• Object detection identifies what and where
• Uses bounding boxes + labels
• Supports multiple objects per image
• Azure AI Vision = prebuilt
• Azure AI Custom Vision = custom models
• Watch for keywords: detect, locate, count, bounding box

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