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| Topic |
Details |
| Topic 1 |
- OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
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| Topic 2 |
- Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
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| Topic 3 |
- Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
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| Topic 4 |
- Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
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| Topic 5 |
- Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
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| Topic 6 |
- Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q10-Q15):
NEW QUESTION # 10
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Modifying the behavior of a pretrained LLM permanently
- B. Training a model on a diverse range of tasks
- C. Providing a few examples of a target task via the input prompt
- D. Teaching a model through zero-shot learning
Answer: C
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 # 11
What are Convolutional Neural Networks (CNNs) primarily used for?
- A. Image classification
- B. Text processing
- C. Time series prediction
- D. Image generation
Answer: A
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 # 12
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. AI, ML, and DL are entirely separate fields with no overlap.
- B. AI is a subset of DL, which is a subset of ML.
- C. ML is a subset of AI, and DL is a subset of ML.
- D. DL is a subset of AI, and ML is a subset of DL.
Answer: C
Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
NEW QUESTION # 13
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Classify documents into different types
- B. Extract text from documents
- C. Extract tables from documents
- D. Generate transcript from documents
Answer: D
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 # 14
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Audio tuning
- B. Profanity filtering
- C. Text normalization
- D. Timestamping
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 # 15
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