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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q18-Q23):

NEW QUESTION # 18
What role do Transformers perform in Large Language Models (LLMs)?

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

Answer: C

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 # 19
What is the primary benefit of using the OCI Language service for text analysis?

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

Answer: A

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 # 20
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Profanity filtering
B. Timestamping
C. Text normalization
D. Audio tuning

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 # 21
What can Oracle Cloud Infrastructure Document Understanding NOT do?

A. Extract tables from documents
B. Generate transcript from documents
C. Classify documents into different types
D. Extract text from documents

Answer: B

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 # 22
What is the primary purpose of reinforcement learning?

A. Learning from outcomes to make decisions
B. Making predictions from labeled data
C. Finding relationships within data sets
D. Identifying patterns in data

Answer: A

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

NEW QUESTION # 23
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100% Pass 2025 Oracle Pass-Sure 1z0-1122-24: Valid Oracle Cloud Infrastructure 2024 AI Foundations Associate Practice Questions

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q18-Q23):

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