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[General] Free PDF Quiz 2026 Oracle 1Z0-1122-25: Updated Oracle Cloud Infrastructure 2025

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【General】 Free PDF Quiz 2026 Oracle 1Z0-1122-25: Updated Oracle Cloud Infrastructure 2025

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Almost everyone is trying to pass the Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) certification exam to upgrade their CVs and land desired jobs. Every applicant of the Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) exam faces just one problem and that is not finding real and Latest 1Z0-1122-25 Exam Questions. Applicants are always confused about where to buy actual 1Z0-1122-25 Exam Questions and prepare successfully for the Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) exam in a short time.
Oracle 1Z0-1122-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Intro to DL Foundations: This section assesses the expertise of Deep Learning Engineers in understanding deep learning frameworks and architectures. It covers fundamental concepts of deep learning, introduces convolutional neural networks (CNN) for image processing, and explores sequence models like recurrent neural networks (RNN) and long short-term memory (LSTM) networks for handling sequential data.
Topic 2
  • Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.
Topic 3
  • Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q34-Q39):NEW QUESTION # 34
What would you use Oracle AI Vector Search for?
  • A. Query data based on semantics.
  • B. Store business data in a cloud database.
  • C. Query data based on keywords.
  • 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 # 35
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 transcribing spoken language
  • D. By analyzing sentiment in text documents
Answer: A
Explanation:
Explanation:

NEW QUESTION # 36
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 # 37
Which AI domain can be employed for identifying patterns in images and extract relevant features?
  • A. Natural Language Processing
  • B. Computer Vision
  • C. Speech Processing
  • D. Anomaly Detection
Answer: B
Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 38
What does "fine-tuning" refer to in the context of OCI Generative AI service?
  • A. Upgrading the hardware of the AI clusters
  • B. Adjusting the model parameters to improve accuracy
  • C. Encrypting the data for security reasons
  • D. Doubling the neural network layers
Answer: B
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 39
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