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| Section | Weight | Objectives |
|---|---|---|
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview |
| Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning |
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Discuss AI Basics - Explain AI vs ML vs DL |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Intro to OCI AI Services | 20% | - OCI Vision - OCI Language - OCI Document Understanding - OCI Speech |
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NEW QUESTION # 17
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 # 18
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: C
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 # 19
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 # 20
What is the purpose of the model catalog in OCI Data Science?
Answer: C
Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
NEW QUESTION # 21
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 # 22
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