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| Section | Weight | Objectives |
|---|---|---|
| Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Document Understanding - OCI Speech |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals |
| 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 Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain Transformers Fundamentals |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
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NEW QUESTION # 40
What feature of OCI Data Science provides an interactive coding environment for building and training models?
Answer: B
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 # 41
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 # 42
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 # 43
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Answer: C
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 # 44
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
Answer: D
Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
NEW QUESTION # 45
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