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| Section | Weight | Objectives |
|---|---|---|
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Intro to Generative AI and LLMs | 15% | - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning - Explain LLM Fine Tuning |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Services Overview - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview |
| 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% | - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
- Discuss Reinforcement Learning Fundamentals |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Vision - OCI Document Understanding - OCI Language |
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NEW QUESTION # 10
What feature of OCI Data Science provides an interactive coding environment for building and training models?
Answer: C
Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
NEW QUESTION # 11
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
Answer: A
Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.
NEW QUESTION # 12
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 # 13
What can Oracle Cloud Infrastructure Document Understanding NOT do?
Answer: B
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
NEW QUESTION # 14
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
Answer: A
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 # 15
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