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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview |
| Topic 2: OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI |
| Topic 3: Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Topic 4: Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
| Topic 5: Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning |
| Topic 6: Intro to OCI AI Services | 20% | - OCI Vision - OCI Speech - OCI Document Understanding - OCI Language |
| Topic 7: Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
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NEW QUESTION # 48
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: A
NEW QUESTION # 49
What would you use Oracle AI Vector Search for?
Answer: D
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 50
What is the purpose of Attention Mechanism in Transformer architecture?
Answer: B
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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NEW QUESTION # 51
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 # 52
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: D
NEW QUESTION # 53
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