1Z0-1122-25 NEW EXAM CAMP, PDF 1Z0-1122-25 FORMAT

1Z0-1122-25 New Exam Camp, Pdf 1Z0-1122-25 Format

1Z0-1122-25 New Exam Camp, Pdf 1Z0-1122-25 Format

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The Oracle Cloud Infrastructure 2025 AI Foundations Associate certification exam is one of the top-rated career advancement 1Z0-1122-25 certifications in the market. This Oracle Cloud Infrastructure 2025 AI Foundations Associate certification exam has been inspiring candidates since its beginning. Over this long period, thousands of Oracle Cloud Infrastructure 2025 AI Foundations Associate exam candidates have passed their 1Z0-1122-25 Certification Exam and now they are doing jobs in the world's top brands.

Oracle 1Z0-1122-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.
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
  • Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.
Topic 4
  • 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.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

  • A. Prevention of harm
  • B. Explicability
  • C. Respect for human autonomy
  • D. Fairness

Answer: B

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 18
What is the difference between classification and regression in Supervised Machine Learning?

  • A. Classification and regression both predict continuous values.
  • B. Classification predicts continuous values, whereas regression assigns data points to categories.
  • C. Classification assigns data points to categories, whereas regression predicts continuous values.
  • D. Classification and regression both assign data points to categories.

Answer: C

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".


NEW QUESTION # 19
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

  • A. Capturing the internal representation of the raw image data
  • B. Directly predicting the final output
  • C. Providing labels for the output neurons
  • D. Storing the input pixel values

Answer: A

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


NEW QUESTION # 20
What would you use Oracle AI Vector Search for?

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

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 # 21
What is the primary purpose of reinforcement learning?

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

Answer: B

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 # 22
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