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| Section | Weight | Objectives |
|---|---|---|
| Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview - Explain Responsible AI - Discuss OCI ML Services Overview |
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Vision - OCI Speech - OCI Language |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning |
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NEW QUESTION # 23
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: B
NEW QUESTION # 24
What is the key feature of Recurrent Neural Networks (RNNs)?
Answer: D
Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
* Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
* Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
* Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by " remembering " past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.
NEW QUESTION # 25
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: A
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 # 26
What is the benefit of using embedding models in OCI Generative AI service?
Answer: C
Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
NEW QUESTION # 27
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
Answer: A
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.
NEW QUESTION # 28
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