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

NEW QUESTION # 27
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. AI is a subset of DL, which is a subset of ML.
B. DL is a subset of AI, and ML is a subset of DL.
C. ML is a subset of AI, and DL is a subset of ML.
D. AI, ML, and DL are entirely separate fields with no overlap.

Answer: C

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 # 28
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 # 29
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification predicts continuous values, whereas regression assigns data points to categories.
B. Classification and regression both predict continuous values.
C. Classification assigns data points to categories, whereas regression predicts continuous values.
D. Classification and regression both assign data points to categories.

Answer: C

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".

NEW QUESTION # 30
What is "in-context learning" in the realm of Large Language Models (LLMs)?

A. Modifying the behavior of a pretrained LLM permanently
B. Providing a few examples of a target task via the input prompt
C. Training a model on a diverse range of tasks
D. Teaching a model through zero-shot learning

Answer: B

Explanation:
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 31
What is the primary purpose of reinforcement learning?

A. Identifying patterns in data
B. Making predictions from labeled data
C. Learning from outcomes to make decisions
D. Finding relationships within data sets

Answer: C

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 # 32
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