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NEW QUESTION # 17
Which AI task involves audio generation from text?
- A. Speech recognition
- B. Text to speech
- C. Text summarization
- D. Audio recording
Answer: B
Explanation:
Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:
Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.
Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.
Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.
Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging. Reference: : Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud, Text-to-speech synthesis - Wikipedia
NEW QUESTION # 18
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Convolutional Neural Network (CNN)
- B. Generative Adversarial Network (GAN)
- C. Recurrent Neural Network (RNN)
- D. Variational Autoencoder (VAE)
Answer: C
Explanation:
Recurrent Neural Networks (RNNs) are a type of deep learning algorithm that can process sequential data, such as sentences, speech, or time series. They are composed of recurrent units that have a loop that allows them to store information from previous inputs and pass it to the next inputs. This way, they can capture the temporal dependencies and context within a sequence. RNNs can be used for various natural language processing tasks, such as text generation, machine translation, sentiment analysis, speech recognition, etc. However, RNNs also suffer from some limitations, such as vanishing or exploding gradients, difficulty in modeling long-term dependencies, and high computational cost. Therefore, some variants and extensions of RNNs have been proposed to overcome these challenges, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional RNN (BiRNN), Attention Mechanism, etc. Reference: : [Recurrent neural network - Wikipedia], [What are Recurrent Neural Networks? | IBM], [Recurrent Neural Network (RNN) in Machine Learning]
NEW QUESTION # 19
What is the primary goal of machine learning?
- A. Creating algorithms to solve complex problems
- B. Improving computer hardware
- C. Explicitly programming computers
- D. Enabling computers to learn and improve from experience
Answer: D
Explanation:
Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. Machine learning algorithms can adapt to new data and situations and improve their performance over time2. Reference: Artificial Intelligence (AI) | Oracle
NEW QUESTION # 20
How is Generative AI different from other AI approaches?
- A. Generative AI understands underlying data and creates new examples.
- B. Generative AI is used exclusively for text-based applications.
- C. Generative AI focuses on decision-making and optimization.
- D. Generative AI generates labeled outputs for training.
Answer: A
Explanation:
Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on the patterns and structure of existing data. Unlike other AI approaches that aim to recognize, classify, or predict data, generative AI aims to generate data that is realistic, diverse, and novel. Generative AI can produce various types of content, such as images, text, audio, video, software code, product designs, and more. Generative AI uses different techniques and models to learn from data and generate new examples, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and foundation models. Generative AI has many applications across different domains and industries, such as art, entertainment, education, healthcare, engineering, marketing, and more. Reference: : Oracle Cloud Infrastructure AI - Generative AI, Generative artificial intelligence - Wikipedia
NEW QUESTION # 21
How does Oracle Cloud Infrastructure Anomaly Detection service contribute to fraud detection?
- A. By identifying abnormal patterns in data
- B. By transcribing spoken language
- C. By generating spoken language from text
- D. By analyzing text sentiment
Answer: A
Explanation:
Oracle Cloud Infrastructure Anomaly Detection is an AI service that provides real-time and batch anomaly detection for univariate and multivariate time series data. Through a simple user interface, organizations can create and train models to detect anomalies and identify unusual behavior, changes in trends, outliers, and more. Anomaly Detection can contribute to fraud detection by analyzing data from various sources, such as transactions, logs, sensors, or customer behavior, and alerting users when suspicious or fraudulent activities are detected2. Reference: Anomaly Detection | Oracle
NEW QUESTION # 22
Which is an application of Generative Adversarial Networks (GANs) in the context of Generative AI?
- A. Prediction of continuous values from Input data
- B. Creation of realistic images that resemble training data
- C. Generation of labeled outputs for training
- D. Classification of data points into categories
Answer: B
Explanation:
Generative Adversarial Networks (GANs) are a type of AI model that can generate realistic images that resemble training data. The architecture of a GAN consists of two separate neural networks that are pitted against each other in a game-like scenario. The first network, known as the generator network, tries to create fake data that looks real. The second network, known as the discriminator network, tries to distinguish between real and fake data. The generator network learns from the feedback of the discriminator network and tries to fool it by improving the quality of the fake data. The discriminator network also learns from the feedback of the generator network and tries to improve its accuracy. The process continues until the generator network produces data that is indistinguishable from the real data4. GANs can be used to create realistic images of faces, animals, landscapes, and more5. Reference: Generative models - OpenAI, Artificial Intelligence Explained: What Are Generative Adversarial ...
NEW QUESTION # 23
Which AI domain is associated with tasks such as recognizing forces in images and classifying objects?
- A. Speech Processing
- B. Natural Language Processing
- C. Anomaly Detection
- D. Computer Vision
Answer: D
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 # 24
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Trains a model from scratch
- B. Customizes the model architecture
- C. Involves post-processing model outputs and optimizing hyper parameters
- D. Guides the model's response using predefined prompts
Answer: D
Explanation:
Prompt engineering is the art of designing natural language instructions or queries that can elicit the desired response from a large language model. Prompt engineering does not modify the model parameters or architecture, but rather relies on the model's existing knowledge and capabilities. Prompt engineering can be used to perform various tasks such as text generation, sentiment analysis, and code completion, by providing the model with the appropriate context, format, and constraints67. Prompt engineering is also known as zero-shot learning or query-based learning. Reference: [2211.01910] Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910), A developer's guide to prompt engineering and LLMs - The GitHub Blog
NEW QUESTION # 25
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Convert tokens into numerical forms (vectors) that the model can understand.
- B. Apply a specific function to each word individually.
- C. Weigh the importance of different words within a sequence and understand the context.
- D. Break down a sentence into smaller pieces called tokens.
Answer: C
Explanation:
The attention mechanism in the Transformer architecture is a technique that allows the model to focus on the most relevant parts of the input and output sequences. It computes a weighted sum of the input or output embeddings, where the weights indicate how much each word contributes to the representation of the current word. The attention mechanism helps the model capture the long-range dependencies and the semantic relationships between words in a sequence12. Reference: The Transformer Attention Mechanism - MachineLearningMastery.com, Attention Mechanism in the Transformers Model - Baeldung
NEW QUESTION # 26
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
- A. Natural Language Processing
- B. Speech Processing
- C. Anomaly Detection
- D. Computer Vision
Answer: A
Explanation:
Natural Language Processing (NLP) is an AI domain that is associated with tasks such as identifying the sentiment of text and translating text between languages. NLP is an interdisciplinary field that combines computer science, linguistics, and artificial intelligence to enable computers to process and understand natural language data, such as text or speech. NLP involves various techniques and applications, such as:
Text analysis: Extracting meaningful information from text data, such as keywords, entities, topics, sentiments, emotions, etc.
Text generation: Producing natural language text from structured or unstructured data, such as summaries, captions, headlines, stories, etc.
Machine translation: Translating text or speech from one language to another automatically and accurately.
Question answering: Retrieving relevant answers to natural language questions from a knowledge base or a document collection.
Speech recognition: Converting speech signals into text or commands.
Speech synthesis: Converting text into speech signals with natural sounding voices.
Natural language understanding: Interpreting the meaning and intent of natural language inputs and generating appropriate responses.
Natural language generation: Creating natural language outputs that are coherent, fluent, and relevant to the context. Reference: : What is Natural Language Processing? | IBM, Natural language processing - Wikipedia
NEW QUESTION # 27
What is the primary purpose of reinforcement learning?
- A. Making predictions from labeled data
- B. Learning from outcomes to make decisions
- C. Identifying patterns in data
- D. Finding relationships within data sets
Answer: B
Explanation:
Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take in each state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:
Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).
Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.
Value function: A function that estimates the expected long-term return of each state or state-action pair, based on the current policy.
Q-learning: A popular reinforcement learning algorithm that learns a value function called Q-function, which represents the quality of taking a certain action in a certain state.
Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high-dimensional problems, such as playing video games or controlling robots. Reference: : Reinforcement learning - Wikipedia, What is Reinforcement Learning? - Overview of How it Works - Synopsys
NEW QUESTION # 28
What is the difference between Large Language Models (LLMs) and traditional machine learning models?
- A. LLMs focus on image recognition tasks.
- B. LLMs require labeled output for training.
- C. LLMs have a limited number of parameters compared to other models.
- D. LLMs are specifically designed for natural language processing and understanding.
Answer: D
Explanation:
Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences. LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes. They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5. Reference: What are LLMs, and how are they used in generative AI?, An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML, An Introduction to Large Language Models (LLMs): How It Got ... - Labellerr
NEW QUESTION # 29
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