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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% | - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Transformers 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 |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) |
| Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Language - OCI Speech - OCI Vision |
| Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
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NEW QUESTION # 41
What feature of OCI Data Science provides an interactive coding environment for building and training models?
Answer: D
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 # 42
What can Oracle Cloud Infrastructure Document Understanding NOT do?
Answer: C
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
NEW QUESTION # 43
What is the purpose of the model catalog in OCI Data Science?
Answer: B
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 # 44
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
Answer: B
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
NEW QUESTION # 45
Which feature is NOT available as part of OCI Speech capabilities?
Answer: C
Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
NEW QUESTION # 46
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