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| Section | Objectives |
|---|
| Topic 1: Oracle AI and Machine Learning Services | - Oracle AI Stack
- 1. AI infrastructure
- 2. AI data and machine learning services
- OCI Machine Learning Services
- 1. Machine learning capabilities in OCI
- 2. OCI Data Science and machine learning workflows
- OCI AI Services
- 1. Vision
- 2. Language
- 3. Speech
- 4. Document Understanding
|
| Topic 2: Generative AI and Large Language Models | - Generative AI Fundamentals
- 1. Generative AI concepts and capabilities
- 2. Generative AI use cases
- Large Language Models
- 1. LLM fundamentals
- 2. Language models and generative AI applications
|
| Topic 3: OCI Generative AI and Oracle Database AI Capabilities | - OCI Generative AI
- 1. OCI Generative AI services
- 2. Generative AI models and applications
- AI Application Frameworks
- 1. Retrieval and AI application concepts
- 2. Language frameworks
- Oracle AI Database
- 1. Vector database concepts
- 2. AI capabilities in Oracle Database
|
| Topic 4: Artificial Intelligence and Machine Learning Fundamentals | - Machine Learning Fundamentals
- 1. Machine learning models and architectures
- 2. Supervised and unsupervised learning
- Deep Learning Fundamentals
- 1. Neural networks
- 2. Convolutional and sequence models
- Artificial Intelligence Concepts
- 1. AI fundamentals and terminology
- 2. AI use cases and applications
|
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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q39-Q44):
NEW QUESTION # 39
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It provides a cost-effective solution for simple AI tasks.
- B. It delivers exceptional performance and scalability for complex AI tasks.
- C. It offers seamless integration with social media platforms.
- D. It is ideal for tasks such as text-to-speech conversion.
Answer: B
Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
NEW QUESTION # 40
What is the purpose of the model catalog in OCI Data Science?
- A. To create and switch between different environments
- B. To deploy models as HTTP endpoints
- C. To provide a preinstalled open source library
- D. To store, track, share, and manage models
Answer: D
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 # 41
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They focus on increasing the number of tokens while keeping the model size constant.
- C. They prioritize larger model sizes to achieve better performance.
- D. They ensure that the model size, training time, and data size are balanced for optimal results.
Answer: D
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 # 42
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They focus on increasing the number of tokens while keeping the model size constant.
- C. They prioritize larger model sizes to achieve better performance.
- D. They ensure that the model size, training time, and data size are balanced for optimal results.
Answer: D
NEW QUESTION # 43
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Speech recognition
- B. Audio recording
- C. Text summarization
- D. Text to speech
Answer: D
Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
NEW QUESTION # 44
......
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