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| Section | Weight | Objectives |
|---|---|---|
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
| Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Transformers Fundamentals |
| Intro to OCI AI Services | 20% | - OCI Vision - OCI Language - OCI Document Understanding - OCI Speech |
| 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 DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals |
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NEW QUESTION # 39
What is " in-context learning " in the realm of Large Language Models (LLMs)?
Answer: D
Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.
NEW QUESTION # 40
How does AI enhance human efforts?
Answer: D
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 # 41
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
Answer: D
Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
NEW QUESTION # 42
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
Answer: A
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 # 43
What is the benefit of using embedding models in OCI Generative AI service?
Answer: A
Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
NEW QUESTION # 44
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