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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| Topic 2: Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
- Discuss Reinforcement Learning Fundamentals |
| Topic 3: Intro to OCI AI Services | 20% | - OCI Vision - OCI Document Understanding - OCI Speech - OCI Language |
| Topic 4: Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview |
| Topic 5: OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search |
| Topic 6: Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Topic 7: Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals |
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NEW QUESTION # 18
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: A
Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.
NEW QUESTION # 19
What are Convolutional Neural Networks (CNNs) primarily used for?
Answer: 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.
NEW QUESTION # 20
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
Answer: D
Explanation:
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.
NEW QUESTION # 21
In machine learning, what does the term " model training " mean?
Answer: B
Explanation:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
NEW QUESTION # 22
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: B
NEW QUESTION # 23
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