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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q300+-Q28):

NEW QUESTION # 300+
What would you use Oracle AI Vector Search for?

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

Answer: A

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 # 24
What role do Transformers perform in Large Language Models (LLMs)?

A. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
B. Image recognition tasks in LLMs
C. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
D. Manually engineer features in the data before training the model

Answer: A

Explanation:
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.

NEW QUESTION # 25
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By automating data extraction from documents
B. By generating lifelike speech from documents
C. By analyzing sentiment in text documents
D. By transcribing spoken language

Answer: A

Explanation:
Explanation:

NEW QUESTION # 26
What is the purpose of the model catalog in OCI Data Science?

A. To create and switch between different environments
B. To provide a preinstalled open source library
C. To deploy models as HTTP endpoints
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 # 27
What is the primary benefit of using the OCI Language service for text analysis?

A. It only works with structured data.
B. It provides image processing capabilities.
C. It allows for text analysis at scale without machine learning expertise.
D. It requires extensive machine learning expertise to use.

Answer: C

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 28
......

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q300+-Q28):

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