100% PASS EFFICIENT 1Z0-1122-25 - TEST ORACLE CLOUD INFRASTRUCTURE 2025 AI FOUNDATIONS ASSOCIATE LAB QUESTIONS

100% Pass Efficient 1Z0-1122-25 - Test Oracle Cloud Infrastructure 2025 AI Foundations Associate Lab Questions

100% Pass Efficient 1Z0-1122-25 - Test Oracle Cloud Infrastructure 2025 AI Foundations Associate Lab Questions

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Tags: Test 1Z0-1122-25 Lab Questions, New 1Z0-1122-25 Exam Answers, 1Z0-1122-25 Exam Introduction, 1Z0-1122-25 Premium Exam, 1Z0-1122-25 Exam Topics Pdf

No software installation is required to go through the web-based Oracle 1Z0-1122-25 practice test. The PDF file of 1Z0-1122-25 real exam questions is easy to use on laptops, tablets, and smartphones. We have added all the Oracle 1Z0-1122-25 Questions, which have a chance to appear in the 1Z0-1122-25 real test. Our Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) dumps PDF exam questions are beneficial to prepare for the test in less time.

Oracle 1Z0-1122-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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.
Topic 5
  • 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.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q24-Q29):

NEW QUESTION # 24
What would you use Oracle AI Vector Search for?

  • A. Query data based on semantics.
  • B. Manage database security protocols.
  • C. Query data based on keywords.
  • 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 # 25
Which AI domain can be employed for identifying patterns in images and extract relevant features?

  • A. Computer Vision
  • B. Speech Processing
  • C. Anomaly Detection
  • D. Natural Language Processing

Answer: A

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 # 26
What can Oracle Cloud Infrastructure Document Understanding NOT do?

  • A. Extract text from documents
  • B. Classify documents into different types
  • C. Extract tables from documents
  • D. Generate transcript from documents

Answer: D

Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .


NEW QUESTION # 27
What is the primary benefit of using the OCI Language service for text analysis?

  • A. It provides image processing capabilities.
  • B. It allows for text analysis at scale without machine learning expertise.
  • C. It only works with structured data.
  • D. It requires extensive machine learning expertise to use.

Answer: B

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


NEW QUESTION # 28
What distinguishes Generative AI from other types of AI?

  • A. Generative AI involves training models to perform tasks without human intervention.
  • B. Generative AI focuses on making decisions based on user interactions.
  • C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
  • D. Generative AI uses algorithms to predict outcomes based on past data.

Answer: C

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.


NEW QUESTION # 29
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