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| Section | Weight | Objectives |
|---|---|---|
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics |
| Intro to Generative AI and LLMs | 15% | - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Language - OCI Vision - OCI Document Understanding |
| OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search |
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NEW QUESTION # 36
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
Answer: D
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 # 37
How does AI enhance human efforts?
Answer: C
Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.
NEW QUESTION # 38
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
Answer: D
Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
* Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.
NEW QUESTION # 39
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
Answer: C
Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
NEW QUESTION # 40
What are Convolutional Neural Networks (CNNs) primarily used for?
Answer: A
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
NEW QUESTION # 41
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