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| Section | Weight | Objectives |
|---|---|---|
| Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning |
| Intro to OCI AI Services | 20% | - OCI Language - OCI Document Understanding - OCI Vision - OCI Speech |
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
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NEW QUESTION # 21
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: B
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 # 22
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: A
NEW QUESTION # 23
Which AI domain can be employed for identifying patterns in images and extract relevant features?
Answer: 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.
NEW QUESTION # 24
What role do Transformers perform in Large Language Models (LLMs)?
Answer: C
Explanation:
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
* Sequential Data Processing in Parallel:
* Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
* This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
* Capturing Long-Range Dependencies:
* Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence.
The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
* This ability to capture long-range dependencies enhances the model ' s understanding of context, leading to more coherent and accurate text generation.
* Applications in LLMs:
* In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
References:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.
NEW QUESTION # 25
What is the difference between classification and regression in Supervised Machine Learning?
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 # 26
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