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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals |
| Topic 2: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Topic 3: Intro to OCI AI Services | 20% | - OCI Speech - OCI Document Understanding - OCI Vision - OCI Language |
| Topic 4: Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals |
| Topic 5: Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data |
| Topic 6: Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview |
| Topic 7: Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning |
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NEW QUESTION # 21
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Answer: A
Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 22
What is the difference between classification and regression in Supervised Machine Learning?
Answer: C
Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .
NEW QUESTION # 23
What can Oracle Cloud Infrastructure Document Understanding NOT do?
Answer: C
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 # 24
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 # 25
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: C
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
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