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Oracle Cloud Infrastructure 2023 AI Foundations Associate Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is the primary purpose of Convolutional Neural Networks (CNNs)?

  • A. Creating music compositions
  • B. Detecting patterns in images
  • C. Processing sequential data
  • D. Generating Images

Answer: B

Explanation:
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. They are made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter, and a feature map. The filter is a small matrix of weights that slides over the input data and performs element-wise multiplication and summation, resulting in a feature map that represents the activation of a certain feature in the input. By applying multiple filters, the CNN can detect different patterns in the image, such as edges, shapes, colors, textures, etc. The pooling layer is used to reduce the spatial dimensionality of the feature maps, while preserving the most important information. The fully connected layer is the final layer of a CNN, and it is where the classification or regression task is performed based on the extracted features. CNNs can learn to detect complex patterns in images by adjusting their weights during training using backpropagation and gradient descent algorithms. Reference: : Convolutional neural network - Wikipedia, What are Convolutional Neural Networks? | IBM, Convolutional Neural Network (CNN) in Machine Learning


NEW QUESTION # 15
Which AI domain is associated with tasks such as recognizing forces in images and classifying objects?

  • A. Anomaly Detection
  • B. Computer Vision
  • C. Speech Processing
  • D. Natural Language Processing

Answer: B

Explanation:
Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are:
Face recognition: Identifying or verifying the identity of a person based on their facial features.
Object detection: Locating and labeling objects of interest in an image or a video.
Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc.
Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc.
Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc.
Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections.
Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes. Reference: : What is Computer Vision? | IBM, Computer vision - Wikipedia


NEW QUESTION # 16
What is the difference between classification and regression in Supervised Machine Learning?

  • A. Classification predicts continuous values, whereas regression assigns data points to categories.
  • B. Classification and regression both predict continuous values.
  • C. Classification and regression both assign data points to categories.
  • D. Classification assigns data points to categories, whereas regression predicts continuous values.

Answer: D

Explanation:
Classification and regression are two subtypes of supervised learning in machine learning. The main difference between them is the type of output variable they deal with. Classification assigns data points to discrete categories based on some criteria or rules. For example, classifying emails into spam or not spam based on their content is a classification problem because the output variable is binary (spam or not spam). Regression predicts continuous values for data points based on their input features. For example, predicting house prices based on their size, location, amenities, etc., is a regression problem because the output variable is continuous (house price). Classification and regression use different types of algorithms and metrics to evaluate their performance. Reference: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, Classification vs Regression in Machine Learning | by ...


NEW QUESTION # 17
Which type of machine learning is used for already labeled data sets?

  • A. Supervised learning
  • B. Active learning
  • C. Unsupervised earning
  • D. Reinforcement learning

Answer: A

Explanation:
Supervised learning is a type of machine learning that uses labeled data sets to train algorithms that can classify data or predict outcomes. Labeled data sets are data sets that have both input features and output labels for each instance. For example, a labeled data set for image classification would have images as input features and the corresponding categories (such as dog, cat, bird, etc.) as output labels. Supervised learning algorithms learn the relationship between the input features and the output labels from the training data set and then use that relationship to make predictions on new or unseen data. Supervised learning can be divided into two subtypes: classification and regression. Classification is the task of assigning discrete categories to data instances, such as spam or not spam for emails. Regression is the task of predicting continuous values for data instances, such as house prices or stock prices. Reference: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, What is Supervised Learning? | IBM


NEW QUESTION # 18
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?

  • A. By automating data extraction from documents
  • B. By generating lifelike speech from text
  • C. By transcribing spoken language
  • D. By analyzing text sentiment

Answer: A

Explanation:
Oracle Cloud Infrastructure Document Understanding service is a cloud-based AI service for automating data extraction from documents. It can process various types of documents, such as invoices, receipts, contracts, forms, etc., and extract key information fields from them using optical character recognition (OCR) and natural language understanding (NLU) techniques. It can also provide confidence scores for each extracted field and enable human verification if needed. By using this service, businesses can reduce manual efforts, improve accuracy, and accelerate workflows that involve document processing. Some of the use cases for Oracle Cloud Infrastructure Document Understanding service are:
Invoice Processing: Extract invoice details, such as invoice number, date, amount, vendor name, etc., and validate them against purchase orders or contracts.
Contract Analysis: Extract contract terms, such as parties, duration, clauses, obligations, etc., and compare them with standard templates or policies.
Form Processing: Extract form fields, such as name, address, phone number, email, etc., and populate them into databases or applications. Reference: : [Document Understanding Overview - Oracle], [AI Document Understanding at Scale | Oracle]


NEW QUESTION # 19
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