Practice Test
True or False: Image classification can be used to automatically tag images based on content.
- True
- False
Answer: True
Explanation: Image classification is a process of AI and machine learning that is used to automatically recognize and tag the content of images.
What is a feature of image classification?
- a) Processing speed
- b) Location of image
- c) Color scheme
- d) Object detection
Answer: d) Object detection
Explanation: Object detection is a feature of image classification where the system is trained to recognize and identify specific objects within an image.
True or False: Image classification always involves manual tagging of images.
- True
- False
Answer: False
Explanation: Though manual tagging can be used in the training process, the goal of image classification is to allow for automatic tagging based on learned patterns and features.
Multiple select: What are the common tasks that you can perform with Azure’s image classification?
- a) Identifying objects in the image
- b) Tagging based on content
- c) Text extraction from images
- d) Changing the color scheme of the image
Answer: a) Identifying objects in the image, b) Tagging based on content, c) Text extraction from images
Explanation: Azure’s image classification excels in object identification, content tagging, and text extraction, but it does not involve changing the color scheme of the image.
True or False: Image classification solutions can be used only with static images.
- True
- False
Answer: False
Explanation: Although primarily used with static images, image classification can also work with real-time video streams, with each frame being independently analyzed.
Which Azure service is best-suited for building image classification models?
- a) Azure Cognitive Services
- b) Azure Machine Learning
- c) Azure Logic Apps
- d) Azure IoT Hub
Answer: a) Azure Cognitive Services
Explanation: Azure Cognitive Services provide pre-built AI models such as Computer Vision for tasks related to image processing like image classification.
Is Image classification a supervised learning task?
- a) Yes
- b) No
Answer: a) Yes
Explanation: Image classification is a supervised learning task, where a model is trained on labeled images and then used to classify new images.
True or False: A higher resolution of an image always leads to better classification results.
- True
- False
Answer: False
Explanation: While a higher resolution can provide more detailed data, it’s not always necessary for successful classification and can sometimes even over-complicate the process.
Which is NOT a feature of image classification?
- a) Object detection
- b) Feature extraction
- c) Real-time processing
- d) Capturing images
Answer: d) Capturing images
Explanation: Image classification involves object detection, feature extraction, and can often involve real-time processing, but it does not capture images.
Does Azure Cognitive Service provide a pre-built image classification model?
- a) Yes
- b) No
Answer: a) Yes
Explanation: Azure Cognitive Service provides a pre-built Computer Vision service for tasks related to image processing, including image classification.
The main goal of image classification is to _______________.
- a) print images
- b) identify the objects and features in an image
- c) increase the size of an image
- d) improve the resolution of the image
Answer: b) identify the objects and features in an image
Explanation: The primary goal of image classification is to identify the objects and features of an image for various applications like object detection, face recognition etc.
A Convolutional Neural Network (CNN) is typically used in ________.
- a) Text processing tasks
- b) Speech recognition tasks
- c) Image classification tasks
- d) None of the above
Answer: c) Image classification tasks
Explanation: CNN is a class of Artificial Neural Networks that has proven very effective in areas such as image recognition and classification.
Interview Questions
What is the purpose of image classification in terms of AI Machine Learning?
Image classification aims to categorize all pixels in a digital image into one of the defined classes. It is a technique in machine learning where predefined labels are assigned to images based on their visual content.
Which Azure service is particularly designed for image classification tasks?
Azure Cognitive Services, specifically the Computer Vision API, is designed for image classification tasks in Microsoft Azure.
What is a key step in preparing data for image classification solutions in Azure?
A key step is labeling the data. This involves assigning each image in your dataset with relevant tags or labels to make it possible for your model to learn from these examples.
How does an image classification model make predictions?
An image classification model makes predictions by looking at the pixel data of the image and identifying patterns that correspond with the classes it was trained to recognize.
Explain the purpose of training an image classification model?
Training an image classification model involves providing the model with a labeled dataset that it uses to learn from. By processing this data, the model learns to recognize patterns that correspond with each label, enabling it to accurately assign labels to new unlabeled images.
What type of datasets is needed for supervised learning in image classification?
Supervised learning in image classification requires labeled datasets. This means that each image in the dataset is tagged with a label that identifies what the image represents.
What is Azure Custom Vision?
Azure Custom Vision is a service of Microsoft Azure’s Cognitive Services Suite that provides image classification and object detection. This service can be trained with just a few images and can update and export models to run offline.
How can Azure Machine Learning be used in image classification solutions?
Azure Machine Learning offers tools and services for creating, training, and deploying machine learning models, including image classification models.
What is multi-label classification in the context of image classification?
Multi-label classification refers to the process of identifying multiple labels in an image. For example, an image might be labeled as containing both a person and a dog.
What are Convolutional Neural Networks (CNN)?
Convolutional Neural Networks (CNNs) are a type of Deep Learning model primarily used for image classification tasks. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from images which makes them highly effective for this type of task.
Can deployed models in Azure Custom Vision be exported for use in other applications?
Yes, Azure Custom Vision allows you to export your trained models to be used in other applications outside of the Azure environment.
How are features in images identified in Convolutional Neural Networks (CNNs)?
CNNs identify features in an image through a series of convolutional, ReLU (Rectified Linear Unit), pooling, fully connected, and loss layers. The convolutional layer applies a series of filters to the image to highlight various features, which are then passed through the subsequent layers to aid in the classification task.
What is object detection in the context of image classification?
Object detection is a computer vision technique that not only classifies what objects are in an image, but also identifies the location of each object in the image.
What is an advantage of using fine-grained classification in image classification?
Fine-grained classification in image classification allows for more detailed, specific classifications. So rather than simply identifying an image as containing a bird, a fine-grained classification could identify the specific species of the bird.
What is the role of Feature Extraction in Image Classification?
Feature extraction involves identifying and isolating various distinctive details in an image. These “features” help to create a more accurate model for classification as they provide more informed distinctions between different categories of images.
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