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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Document Understanding - OCI Speech |
| Topic 2: Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview |
| Topic 3: Intro to Generative AI and LLMs | 15% | - Explain Prompt Engineering and Instruction Tuning - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Discuss Large Language Models Fundamentals |
| Topic 4: Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Topic 5: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Topic 6: Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals |
| Topic 7: Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics |
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NEW QUESTION # 27
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Answer: D
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 # 28
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
Answer: B
Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.
NEW QUESTION # 29
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?
Answer: B
Explanation:
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.
NEW QUESTION # 30
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: A
NEW QUESTION # 31
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 # 32
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