FREE PDF QUIZ 2025 TRUSTABLE 1Z0-1122-25: ORACLE CLOUD INFRASTRUCTURE 2025 AI FOUNDATIONS ASSOCIATE FREE BRAIN DUMPS

Free PDF Quiz 2025 Trustable 1Z0-1122-25: Oracle Cloud Infrastructure 2025 AI Foundations Associate Free Brain Dumps

Free PDF Quiz 2025 Trustable 1Z0-1122-25: Oracle Cloud Infrastructure 2025 AI Foundations Associate Free Brain Dumps

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q35-Q40):

NEW QUESTION # 35
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

  • A. Document Understanding
  • B. Language
  • C. Vision
  • D. Speech

Answer: D

Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the "Speech" service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


NEW QUESTION # 36
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 # 37
Which feature is NOT available as part of OCI Speech capabilities?

  • A. Supports multiple languages including English, Spanish, and Portuguese
  • B. Transcribes audio and video files into text
  • C. Provides timestamped, grammatically accurate transcriptions
  • D. Uses extensive data science experience to operate

Answer: D

Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs. These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.


NEW QUESTION # 38
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

  • A. It provides real-time translation of text.
  • B. It converts audio files into text.
  • C. It recognizes and extracts text from a document.
  • D. It enhances the visual quality of documents.

Answer: C

Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.


NEW QUESTION # 39
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

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

Answer: A

Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.


NEW QUESTION # 40
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