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NEW QUESTION # 15
What is the purpose of fine-tuning Large Language Models?
- A. To reduce the number of parameters in the model
- B. To Increase the complexity of the model architecture
- C. To specialize the model's capabilities for specific tasks
- D. To prevent the model from overfitting
Answer: C
Explanation:
Fine-tuning is the process of updating the model parameters on a new task and dataset, using a pre-trained large language model as the starting point. Fine-tuning allows the model to adapt to the specific context and domain of the new task, and improve its performance and accuracy. Fine-tuning can be used to customize the model's capabilities for specific tasks such as text classification, named entity recognition, and machine translation82. Fine-tuning is also known as transfer learning or task-based learning. Reference: A Complete Guide to Fine Tuning Large Language Models, Finetuning Large Language Models - DeepLearning.AI
NEW QUESTION # 16
As an IT manager for your company, you are responsible for migrating your company's image and video analysis workloads to Oracle Cloud Infrastructure (OCI). Your team is particularly interested in a cloud service that offers advanced computer vision capabilities, including custom model training.
Which OCI service would you consider for this purpose?
- A. OCI Document Understanding
- B. OCI Vision
- C. OCI Speech
- D. OCI Language
Answer: B
Explanation:
OCI Vision is the best choice for migrating your company's image and video analysis workloads to Oracle Cloud Infrastructure, as it offers advanced computer vision capabilities, including custom model training. With OCI Vision, you can build your own models to detect and classify objects in images and videos, using your own data and labels. You can also use OCI Vision's pretrained models for common tasks such as face detection, face recognition, and face analysis. OCI Vision supports various file formats, such as JPG, PNG, PDF, and TIFF, and can connect to many data sources, such as Object Storage, Autonomous Transaction Processing, and InfluxDB3. Reference: Vision - Oracle
NEW QUESTION # 17
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Variational Autoencoder (VAE)
- B. Generative Adversarial Network (GAN)
- C. Recurrent Neural Network (RNN)
- D. Convolutional Neural Network (CNN)
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 # 18
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. Binary Classification
- C. Multi-Class Classification
- D. Regression
Answer: C
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 # 19
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
- A. Natural Language Processing
- B. Computer Vision
- C. Speech Processing
- D. Anomaly Detection
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 # 20
Which AI domain is associated with tasks such as recognizing forces in images and classifying objects?
- A. Natural Language Processing
- B. Computer Vision
- C. Speech Processing
- D. Anomaly Detection
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 # 21
Which type of machine learning is used for already labeled data sets?
- A. Supervised learning
- B. Reinforcement learning
- C. Unsupervised earning
- D. Active 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 # 22
What is the advantage of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It provides a cost-effective solution for simple AI tasks.
- B. It offers seamless integration with social media platforms.
- C. It delivers exceptional performance and scalability for complex AI tasks.
- D. It is ideal for tasks such as text-to-speech conversion.
Answer: C
Explanation:
Oracle Cloud Infrastructure Supercluster is a cloud service that provides ultrafast cluster networking, HPC storage, and OCI Compute bare metal instances. OCI Supercluster is ideal for training generative AI, including conversational applications and diffusion models, as it can deploy up to tens of thousands of NVIDIA GPUs per cluster for much greater scalability than similar offerings from other providers. OCI Supercluster also reduces the time needed to train AI models with simple Ethernet network architecture that provides ultrahigh performance at massive scale. Additionally, OCI Supercluster offers cost savings and access to AI subject matter experts56. Reference: OCI Supercluster and AI Infrastructure | Oracle, Oracle Delivers More Choices for AI Infrastructure and General-Purpose ...
NEW QUESTION # 23
You are the lead developer of a Deep Learning research team, and you are tasked with improving the training speed of your deep neural networks. To accelerate the training process, you decide to leverage specialized hardware.
Which hardware component is commonly used in Deep Learning to accelerate model training?
- A. Central Processing Unit (CPU)
- B. Graphics Processing Unit (GPU)
- C. Random Access Memory (RAM)
- D. Solid-State Drive (SSD)
Answer: B
Explanation:
A graphics processing unit (GPU) is a specialized hardware component that can perform parallel computations on large amounts of data. GPUs are widely used in deep learning to accelerate the training of deep neural networks, as they can execute many matrix operations and tensor operations simultaneously. GPUs can significantly reduce the training time and improve the performance of deep learning models compared to using CPUs alone678. Reference: Hardware Recommendations for Machine Learning / AI, New hardware offers faster computation for artificial intelligence ..., The Best Hardware for Machine Learning - ReHack, Hardware for Deep Learning Inference: How to Choose the Best One for ...
NEW QUESTION # 24
What is "in-context learning" in the realm of large Language Models (LLMs)?
- A. Training a model on a diverse range of tasks
- B. Providing a few examples of a target task via the input prompt
- C. Modifying the behavior of a pretrained LLM permanently
- D. Teaching a mode! through zero-shot learning
Answer: B
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 # 25
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 # 26
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Guides the model's response using predefined prompts
- B. Customizes the model architecture
- C. Involves post-processing model outputs and optimizing hyper parameters
- D. Trains a model from scratch
Answer: A
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 # 27
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