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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is "in-context learning" in the realm of Large Language Models (LLMs)?

A. Training a model on a diverse range of tasks
B. Providing a few examples of a target task via the input prompt
C. Teaching a model through zero-shot learning
D. Modifying the behavior of a pretrained LLM permanently

Answer: B

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 # 12
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 is ideal for tasks such as text-to-speech conversion.
C. It delivers exceptional performance and scalability for complex AI tasks.
D. It offers seamless integration with social media platforms.

Answer: C

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 # 13
What would you use Oracle AI Vector Search for?

A. Query data based on keywords.
B. Query data based on semantics.
C. Manage database security protocols.
D. Store business data in a cloud database.

Answer: B

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 14
What distinguishes Generative AI from other types of AI?

A. Generative AI uses algorithms to predict outcomes based on past data.
B. Generative AI focuses on making decisions based on user interactions.
C. Generative AI involves training models to perform tasks without human intervention.
D. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.

Answer: D

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

NEW QUESTION # 15
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Text to speech
B. Text summarization
C. Speech recognition
D. Audio recording

Answer: A

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 # 16
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