RELIABLE 1Z0-1122-25 EXAM PRACTICE & 1Z0-1122-25 CERTIFICATION EXAM DUMPS

Reliable 1Z0-1122-25 Exam Practice & 1Z0-1122-25 Certification Exam Dumps

Reliable 1Z0-1122-25 Exam Practice & 1Z0-1122-25 Certification Exam Dumps

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q29-Q34):

NEW QUESTION # 29
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

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

Answer: D

Explanation:
Explanation:


NEW QUESTION # 30
What is the primary purpose of reinforcement learning?

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

Answer: C

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 # 31
What is the benefit of using embedding models in OCI Generative AI service?

  • A. They facilitate semantic searches.
  • B. They optimize the use of computational resources.
  • C. They simplify managing databases.
  • D. They enable creating detailed graphics.

Answer: A

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .


NEW QUESTION # 32
What are Convolutional Neural Networks (CNNs) primarily used for?

  • A. Image classification
  • B. Text processing
  • C. Time series prediction
  • D. Image generation

Answer: A

Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.


NEW QUESTION # 33
Which capability is supported by Oracle Cloud Infrastructure Language service?

  • A. Converting text into images
  • B. Translating text into speech
  • C. Analyzing text to extract structured information like sentiment or entities
  • D. Detecting objects and scenes in images

Answer: C

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


NEW QUESTION # 34
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