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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Topic 2: Intro to OCI AI Services | 20% | - OCI Speech - OCI Vision - OCI Language - OCI Document Understanding |
| Topic 3: Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) |
| Topic 4: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Topic 5: Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics |
| Topic 6: Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning - Explain Transformers Fundamentals |
| Topic 7: 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 |
>> 1z0-1122-26 Exam Actual Questions <<
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NEW QUESTION # 32
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: B
NEW QUESTION # 33
What is " in-context learning " in the realm of Large Language Models (LLMs)?
Answer: B
Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.
NEW QUESTION # 34
What is the key feature of Recurrent Neural Networks (RNNs)?
Answer: C
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 # 35
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Answer: A
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 # 36
What does " fine-tuning " refer to in the context of OCI Generative AI service?
Answer: A
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
NEW QUESTION # 37
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