Test 1222-24

What key objective does machine learning strive to achieve?. Explicitly programming computers. Enabling computers to learn and improve from experience. Improving computer hardware. Creating algorithms to solve complex problems.

In machine learning, what does the term "model training" mean?. Analyzing the accuracy of a trained model. Establishing a relationship between input features and output. Performing data analysis on collected and labeled data. Writing code for the entire program.

What is "in-context learning" in the realm of Large Language Models (LLMs)?. Providing a few examples of a target task via the input prompt. Training a model on a diverse range of tasks. Modifying the behavior of a pretrained LLM permanently. Teaching a model through zero-shot learning.

How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure. Both involve retraining the model, but Prompt Engineering does it more often.

Which is NOT a category of pretrained foundational models available in the OCI Generative Al service?. Generation models. Chat models. Translation models. Embedding models.

What is a key advantage of using dedicated Al clusters in the OCI Generative Al service?. They provide high performance compute resources for fine-tuning tasks. They are free of charge for all users. They provide faster internet connection speeds. They allow access to unlimited database resources.

What does "fine-tuning" refer to in the context of OCI Generative Al service?. Encrypting the data for security reasons. Upgrading the hardware of the Al clusters. Adjusting the model parameters to improve accuracy. Doubling the neural network layers.

What is the benefit of using embedding models in OCI Generative Al service?. They simplify managing databases. They facilitate semantic searches. They optimize the use of computational resources. They enable creating detailed graphics.

You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' linesses upon adinission to a hospital. The goof is to classify petere componistew Model Ingh Itisk based on their medical bistory and vital signs Which type of supervised learning algorithm is required in this scenario. Regression. Binary Classification. Clustering. Multi-Class Classification.

What is the difference between classification and regression in Supervised Machine Learning?. Classification and regression both assign data points to categories. Classification and regression both predict continuous values. Classification assigns data points to categories, whereas regression predicts continuous values. Classification predicts continuous values, whereas regression assigns data points to categories.

Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). Gradient Descent. Support Vector Machine. Backpropagation. Random Forest.

What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?. Capturing the internal representation of the raw image data. Storing the input pixel values. Directly predicting the final output. Providing labels for the output neurons.

You are part of the medical transcription team and need to automate transcription tasks. Which OCI Al service are you most likely to use?. Language. Document Understanding. Speech. Vision.

How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?. By automating data extraction from documents. By transcribing spoken language. By analyzing sentiment in text documents. By generating lifelike speech from documents.

Which capability is supported by the Oracle Cloud Infrastructure Vision service?. Analyzing historical data for unusual patterns. Generating realistic images from text. Detecting vehicle number plates to issue speed citations. Detecting and preventing fraud in financial transactions.

Which feature is NOT available as part of OCI Speech capabilities?. Transcribes audio and video files into text. Supports multiple languages including English, Spanish, and Portuguese. Uses extensive data science experience to operate. Provides timestamped, grammatically accurate transcriptions.

Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?. It provides real-time translation of text. It enhances the visual quality of documents. It converts audio files into text. It recognizes and extracts text from a document.

Which feature of OCI Speech helps make transcriptions easier to read and understand?. Text normalization. Audio tuning. Timestamping. Profanity filtering.

Which capability is supported by Oracle Cloud Infrastructure Language service?. Converting text into images. Detecting objects and scenes in images. Translating text into speech. Analyzing text to extract structured information like sentiment or entities.

What can Oracle Cloud Infrastructure Document Understanding NOT do?. Generate transcript from documents. Classify documents into different types. Extract text from documents. Extract tables from documents.

What distinguishes Generative Al from other types of Al?. Generative Al uses algorithms to predict outcomes based on past data. Generative Al focuses on making decisions based on user interactions. Generative Al involves training models to perform tasks without human intervention. Generative Al creates diverse content such as text, audio, and images by learning patterns from existing data.

Which Al Ethics principle leads to the Responsible Al requirement of transparency?. Respect for human autonomy. Explicability. Fairness. Prevention of harm.

What is the primary purpose of reinforcement learning?. Identifying patterns in data. Finding relationships within data sets. Making predictions from labeled data. Learning from outcomes to make decisions.

How do Large Language Models (LLMS) handle the trade-off between model size, data quality, data size and performance?. They prioritize larger model sizes to achieve better performance. They focus on increasing the number of tokens while keeping the model size constant. They ensure that the model size, training time, and data size are balanced for optimal results. They disregard model size and prioritize high-quality data only.

What is the key feature of Recurrent Neural Networks (RNNs)?. They have a feedback loop that allows information to persist across different time steps. They are primarily used for image recognition tasks. They do not have an internal state. They process data in parallel.

What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for Al workloads?. It offers seamless integration with social media platforms. It is ideal for tasks such as t-to-speech conversion. It delivers exceptional performance and scalability for complex Al tasks. It provides a cost-effective solution for simple Al tasks.

Which Al domain can be employed for identifying patterns in images and extract relevant features?. Natural Language Processing. Computer Vision. Speech Processing. Anomaly Detection.

Which Al domain is associated with tasks such as identifying the sentiment of text and translating text between languages?. Anomaly Detection. Speech Processing. Computer Vision. Natural Language Processing.

Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?. Unsupervised learning. Supervised learning. Reinforcement learning. Active learning.

What are Convolutional Neural Networks (CNNs) primarily used for?. Time series prediction. Image classification. Image generation. Text processing.

Which statement best describes the relationship between Artificial Intelligence (Al), Machine Learning (ML), and Deep Learning (DL)?. ML is a subset of Al, and DL is a subset of ML. Al is a subset of DL, which is a subset of ML. DL is a subset of Al, and ML is a subset of DL. DL are entirely separate fields with no overlap.

What is the purpose of the model catalog in OCI Data Science?. To store, track, share, and manage models. To provide a preinstalled open source library. To deploy models as HTTP endpoints. To create and switch between different environments.

What feature of OCI Data Science provides an interactive coding environment for building and training models?. Notebook sessions. Accelerated Data Science (ADS) SDK. O Model catalog. Conda environment.

7. Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?. Language Detection. Text Classification. Sentiment Analysis. Text Generation.

6. Which is NOT a capability of OCI Vision's image analysis?. Locating and extracting text in images. Translating text in images to another language. Assigning classification labels to images. Object detection with bounding boxes.

5. What is the primary benefit of using the OCI Language service for text analysis?. It allows for text analysis at scale without machine learning expertise. It requires extensive machine learning expertise to use. It only works with structured data. OIt provides image processing capabilities.

4. What would you use Oracle Al Vector Search for?. Query data based on semantics. Store business data in a cloud database. Query data based on keywords. Manage database security protocols.

3. How does Al enhance human efforts?. By deleting data humans need to handle. By processing data at a speed and effectiveness far beyond human capability. By completely replacing human workers in all tasks. By increasing the physical strength of humans.

2. What role do Transformers perform in Large Language Models (LLMs)?. Image recognition tasks in LLMS. Provide a mechanism to process sequential data in parallel and capture long-range dependencies. Manually engineer features in the data before training the model. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints.

1. What is the purpose of Attention Mechanism in Transformer architecture?. Weigh the importance of different words within a sequence and understand the context. Apply a specific function to each word individually. Break down a sentence into smaller pieces called tokens. Convert tokens into numerical forms (vectors) that the model can understand.