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| Section | Weight | Objectives |
|---|---|---|
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals |
| Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Speech - OCI Document Understanding |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| 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 # 24
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: A
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 # 25
What are Convolutional Neural Networks (CNNs) primarily used for?
Answer: D
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
NEW QUESTION # 26
What would you use Oracle AI Vector Search for?
Answer: C
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 27
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: C
Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.
NEW QUESTION # 28
What feature of OCI Data Science provides an interactive coding environment for building and training models?
Answer: A
Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
NEW QUESTION # 29
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