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Oracle 1Z0-1122-25 Exam Dumps

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Description

Exam Name: Oracle Cloud Infrastructure 2025 AI Foundations Associate
Exam Code: 1Z0-1122-25
Related Certification(s):

  • Oracle Cloud Certifications
  • Oracle Cloud Infrastructure Certifications
Certification Provider: Oracle
Actual Exam Duration: 60 Minutes
Number of 1Z0-1122-25 practice questions in our database: 41
Expected 1Z0-1122-25 Exam Topics, as suggested by Oracle :

  • Module 1: Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.
  • Module 2: Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
  • Module 3: 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.
  • Module 4: 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.
  • Module 5: 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.
  • Module 6: 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.
  • Module 7: 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.

Description

Exam Name: Oracle Cloud Infrastructure 2025 AI Foundations Associate
Exam Code: 1Z0-1122-25
Related Certification(s):

  • Oracle Cloud Certifications
  • Oracle Cloud Infrastructure Certifications
Certification Provider: Oracle
Actual Exam Duration: 60 Minutes
Number of 1Z0-1122-25 practice questions in our database: 41
Expected 1Z0-1122-25 Exam Topics, as suggested by Oracle :

  • Module 1: Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.
  • Module 2: Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
  • Module 3: 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.
  • Module 4: 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.
  • Module 5: 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.
  • Module 6: 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.
  • Module 7: 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.

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Q1. What can Oracle Cloud Infrastructure Document Understanding NOT do?

A.Generate transcript from documents

B. Extract tables from documents

C. Classify documents into different types

D. Extract text from documents

Correct Answer: A

Q2. What feature of OCI Data Science provides an interactive coding environment for building and training models?

A.Accelerated Data Science (ADS) SDK

B. Conda environment

C. Model catalog

D. Notebook sessions

Correct Answer: D

Q3. What is the key feature of Recurrent Neural Networks (RNNs)?

A.They process data in parallel.

B. They are primarily used for image recognition tasks.

C. They have a feedback loop that allows information to persist across different time steps.

D. They do not have an internal state.

Correct Answer: C

Q4. What can Oracle Cloud Infrastructure Document Understanding NOT do?

A.Generate transcript from documents

B. Extract tables from documents

C. Classify documents into different types

D. Extract text from documents

Correct Answer: A

Q5. What would you use Oracle AI Vector Search for?

A.Store business data in a cloud database.

B. Manage database security protocols.

C. Query data based on keywords.

D. Query data based on semantics.

Correct Answer: D

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