What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
C
Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI)
Generative AI service is the provision of high-performance compute resources that are specifically
optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained
models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI
are designed to deliver the necessary performance and scalability to handle the intense workloads
associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster
processing and more efficient training.
You are working on a project for a healthcare organization that wants to develop a system to predict
the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into
three categories – Low Risk, Moderate Risk, and High Risk – based on their medical history and vital
signs. Which type of supervised learning algorithm is required in this scenario?
B
Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories—Low Risk,
Moderate Risk, and High Risk—based on their medical history and vital signs, a Multi-Class
Classification algorithm is required. Multi-class classification is a type of supervised learning
algorithm used when there are three or more classes or categories to predict. This method is well-
suited for situations where each instance needs to be classified into one of several categories, which
aligns with the requirement to categorize patients into different risk levels.
What is the purpose of Attention Mechanism in Transformer architecture?
A
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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How does AI enhance human efforts?
D
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.
Which algorithm is primarily used for adjusting the weights of connections between neurons during
the training of an Artificial Neural Network (ANN)?
B
Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between
neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning
algorithm that calculates the gradient of the loss function with respect to each weight by applying
the chain rule, propagating the error backward from the output layer to the input layer. This process
updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights
based on the gradients computed by backpropagation, but backpropagation is the specific method
used to calculate these gradients.
What are Convolutional Neural Networks (CNNs) primarily used for?
A
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks
involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their
ability to detect features such as edges, textures, and shapes across multiple layers of convolutional
filters. This makes them the model of choice for tasks such as object recognition, image
segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their
primary application remains in image classification.
Which statement best describes the relationship between Artificial Intelligence (AI), Machine
Learning (ML), and Deep Learning (DL)?
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.
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when
recognizing handwritten digits?
A
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.
Which AI domain can be employed for identifying patterns in images and extract relevant features?
D
Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and
extracting relevant features. This field focuses on enabling machines to interpret and understand
visual information from the world, automating tasks that the human visual system can perform, such
as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision
are widely used in applications ranging from facial recognition and image classification to medical
image analysis and autonomous vehicles.
What is the difference between classification and regression in Supervised Machine Learning?
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?".