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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 2: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 3: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Topic 4: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 5: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 6: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 7: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
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NEW QUESTION # 38
What is the primary purpose of reinforcement learning?
Answer: D
Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.
NEW QUESTION # 39
How does AI enhance human efforts?
Answer: C
Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.
NEW QUESTION # 40
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
Answer: D
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 # 41
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: A
Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 42
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: D
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 # 43
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