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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) |
| Topic 2: Intro to OCI AI Services | 20% | - OCI Speech - OCI Vision - OCI Language - OCI Document Understanding |
| Topic 3: Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Topic 4: Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning |
| Topic 5: Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Topic 6: OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI |
| Topic 7: Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview |
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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 a key advantage of using dedicated AI clusters in the OCI Generative AI service?
Answer: A
Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
NEW QUESTION # 29
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: B
NEW QUESTION # 30
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: A
NEW QUESTION # 31
A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?
Answer: A
Explanation:
OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.
NEW QUESTION # 32
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