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| Section | Weight | Objectives |
|---|---|---|
| Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI |
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Discuss AI Basics - Explain AI vs ML vs DL |
| Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview |
| Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Language - OCI Vision - OCI Speech |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
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NEW QUESTION # 27
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
Answer: B
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 # 28
What is the primary benefit of using the OCI Language service for text analysis?
Answer: D
Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.
NEW QUESTION # 29
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: A
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 # 30
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 # 31
Which capability is supported by Oracle Cloud Infrastructure Language service?
Answer: C
Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.
NEW QUESTION # 32
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