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| Section | Weight | Objectives |
|---|---|---|
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Vision - OCI Language - OCI Document Understanding |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI |
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Explain Transformers Fundamentals |
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NEW QUESTION # 25
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: B
Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.
NEW QUESTION # 26
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 # 27
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: A
Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 28
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
Answer: D
Explanation:
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.
NEW QUESTION # 29
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: D
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 # 30
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