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| Section | Weight | Objectives |
|---|---|---|
| Intro to OCI AI Services | 20% | - OCI Vision - OCI Document Understanding - OCI Speech - OCI Language |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) |
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
| Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics |
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NEW QUESTION # 26
What is the key feature of Recurrent Neural Networks (RNNs)?
Answer: B
Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
* Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
* Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
* Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by " remembering " past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.
NEW QUESTION # 27
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
Answer: B
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.
NEW QUESTION # 28
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
Answer: B
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
NEW QUESTION # 29
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Answer: B
Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.
NEW QUESTION # 30
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: B
Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
NEW QUESTION # 31
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