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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 assign data points to categories.
C. Classification assigns data points to categories, whereas regression predicts continuous values.
D. Classification and regression both predict continuous values.

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 # 13
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 # 14
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They prioritize larger model sizes to achieve better performance.
B. They ensure that the model size, training time, and data size are balanced for optimal results.
C. They disregard model size and prioritize high-quality data only.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: B

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 15
What is the purpose of Attention Mechanism in Transformer architecture?

A. Convert tokens into numerical forms (vectors) that the model can understand.
B. Break down a sentence into smaller pieces called tokens.
C. Weigh the importance of different words within a sequence and understand the context.
D. Apply a specific function to each word individually.

Answer: C

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 16
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

A. Storing the input pixel values
B. Capturing the internal representation of the raw image data
C. Providing labels for the output neurons
D. Directly predicting the final output

Answer: B

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.

NEW QUESTION # 17
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