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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 2: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Topic 3: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 4: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 5: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 6: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 7: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
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NEW QUESTION # 40
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
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 # 41
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Answer: A
Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 42
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
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 # 43
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 # 44
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
Answer: D
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.
NEW QUESTION # 45
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