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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

A. Generating realistic images from text
B. Analyzing historical data for unusual patterns
C. Detecting vehicle number plates to issue speed citations
D. Detecting and preventing fraud in financial transactions

Answer: C

Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.

NEW QUESTION # 28
What key objective does machine learning strive to achieve?

A. Improving computer hardware
B. Enabling computers to learn and improve from experience
C. Creating algorithms to solve complex problems
D. Explicitly programming computers

Answer: B

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 29
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Generation models
B. Translation models
C. Chat models
D. Embedding models

Answer: B

Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.

NEW QUESTION # 30
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Doubling the neural network layers
B. Adjusting the model parameters to improve accuracy
C. Encrypting the data for security reasons
D. Upgrading the hardware of the AI clusters

Answer: B

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 31
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Unsupervised learning
B. Supervised learning
C. Active learning
D. Reinforcement learning

Answer: A

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 # 32
......

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Lucas Hughes Lucas Hughes

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