Verified 1z0-1122-23 dumps Q&As - Pass Guarantee Exam Dumps Test Engine [2023]
1z0-1122-23 dumps and 30 unique questions
NEW QUESTION # 12
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 analyzing text sentiment
- D. By generating spoken language from text
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 # 13
What is "in-context learning" in the realm of large Language Models (LLMs)?
- A. Providing a few examples of a target task via the input prompt
- B. Training a model on a diverse range of tasks
- C. Modifying the behavior of a pretrained LLM permanently
- D. Teaching a mode! through zero-shot learning
Answer: A
Explanation:
In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. Reference: [2307.12375] In-Context Learning in Large Language Models Learns Label ...](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models](https://arxiv.org/abs/2307.07164)
NEW QUESTION # 14
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Generative Adversarial Network (GAN)
- B. Variational Autoencoder (VAE)
- C. Convolutional Neural Network (CNN)
- D. Recurrent Neural Network (RNN)
Answer: D
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 # 15
Which type of machine learning is used for already labeled data sets?
- A. Unsupervised earning
- B. Supervised learning
- C. Reinforcement learning
- D. Active learning
Answer: B
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 # 16
How is Generative AI different from other AI approaches?
- A. Generative AI focuses on decision-making and optimization.
- B. Generative AI is used exclusively for text-based applications.
- C. Generative AI generates labeled outputs for training.
- D. Generative AI understands underlying data and creates new examples.
Answer: D
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 # 17
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?
- A. By generating lifelike speech from text
- B. By transcribing spoken language
- C. By analyzing text sentiment
- D. By automating data extraction from documents
Answer: D
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 # 18
Which NVIDIA GPU is offered by Oracle Cloud Infrastructure?
- A. A100
- B. P200
- C. K80
- D. T4
Answer: A
Explanation:
Oracle Cloud Infrastructure offers NVIDIA A100 Tensor Core GPUs as one of the GPU options for its compute instances. The NVIDIA A100 GPU is a powerful and versatile GPU that can accelerate a wide range of AI and HPC workloads. The A100 GPU delivers up to 20x higher performance than the previous generation V100 GPU and supports features such as multi-instance GPU, automatic mixed precision, and sparsity acceleration12. The OCI Compute bare-metal BM.GPU4.8 instance offers eight 40GB NVIDIA A100 GPUs linked via high-speed NVIDIA NVLink direct GPU-to-GPU interconnects3. This instance is ideal for training large language models, computer vision models, and other complex AI tasks. Reference: Accelerated Computing and Oracle Cloud Infrastructure (OCI) - NVIDIA, Oracle Cloud Infrastructure Offers New NVIDIA GPU-Accelerated Compute ..., GPU, Virtual Machines and Bare Metal | Oracle
NEW QUESTION # 19
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Translating speech into text
- B. Converting text into images
- C. Detecting objects and scenes in Images
- D. Analyzing text to extract structured information like sentiment or entities
Answer: D
Explanation:
Oracle Cloud Infrastructure Language service is a cloud-based AI service for performing sophisticated text analysis at scale. It provides various capabilities to process unstructured text and extract structured information like sentiment or entities using natural language processing techniques. Some of the capabilities supported by Oracle Cloud Infrastructure Language service are:
Language Detection: Detects languages based on the provided text, and includes a confidence score.
Text Classification: Identifies the document category and subcategory that the text belongs to.
Named Entity Recognition: Identifies common entities, people, places, locations, email, and so on.
Key Phrase Extraction: Extracts an important set of phrases from a block of text.
Sentiment Analysis: Identifies aspects from the provided text and classifies each into positive, negative, or neutral polarity.
Text Translation: Translates text into the language of your choice.
Personal Identifiable Information: Identifies, classifies, and de-identifies private information in unstructured text Reference: : Language Overview - Oracle, AI Text Analysis at Scale | Oracle
NEW QUESTION # 20
What is the primary function of Oracle Cloud Infrastructure Speech service?
- A. Transcribing spoken language into written text
- B. Recognizing objects in images
- C. Converting text into images
- D. Analyzing sentiment n text
Answer: A
Explanation:
Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech's pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session. With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1. Reference: Speech AI Service that Uses ASR | OCI Speech - Oracle
NEW QUESTION # 21
What is the primary goal of machine learning?
- A. Explicitly programming computers
- B. Creating algorithms to solve complex problems
- C. Improving computer hardware
- 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 # 22
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs.
Which type of supervised learning algorithm is required in this scenario?
- A. Clustering
- B. Multi-Class Classification
- C. Binary Classification
- D. Regression
Answer: B
Explanation:
Multi-class classification is a type of supervised learning algorithm that is required in this scenario because the output variable has more than two classes. Multi-class classification is the problem of classifying instances into one of three or more classes. For example, classifying patients into low risk, moderate risk, or high risk based on their medical history and vital signs is a multi-class classification problem because each patient can only belong to one of these three classes. Multi-class classification can be solved by using various algorithms, such as decision trees, random forests, support vector machines (SVMs), k-nearest neighbors (k-NN), naive Bayes, logistic regression, neural networks, etc. Some of these algorithms can naturally handle multi-class problems, while others need to be adapted by using strategies such as one-vs-one or one-vs-rest. Reference: : Multiclass classification - Wikipedia, Multiclass Classification- Explained in Machine Learning
NEW QUESTION # 23
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Detecting and classifying objects in images
- B. Generating realistic Images from text
- C. Detecting and preventing fraud in financial transactions
- D. Analyzing historical data for unusual patterns
Answer: A
Explanation:
Oracle Cloud Infrastructure Vision is a serverless, multi-tenant service, accessible using the Console, or over REST APIs. You can upload images to detect and classify objects in them. If you have lots of images, you can process them in batch using asynchronous API endpoints. Vision's features are thematically split between Document AI for document-centric images, and Image Analysis for object and scene-based images. Image Analysis supports both pretrained and custom models for object detection and image classification3. Reference: Vision - Oracle
NEW QUESTION # 24
......
1z0-1122-23 Dumps for Pass Guaranteed - Pass 1z0-1122-23 Exam: https://www.briandumpsprep.com/1z0-1122-23-prep-exam-braindumps.html
1z0-1122-23 Exam Dumps - Try Best 1z0-1122-23 Exam Questions: https://drive.google.com/open?id=1a3djt2hbFaJ4tZ5Y2ltZ4YGG_F0KYSzu
