1Z0-1122-25 RELIABLE EXAM ANSWERS HELP YOU PASS THE 1Z0-1122-25 EXAM EASILY

1Z0-1122-25 Reliable Exam Answers Help You Pass the 1Z0-1122-25 Exam Easily

1Z0-1122-25 Reliable Exam Answers Help You Pass the 1Z0-1122-25 Exam Easily

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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
  • 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 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.
Topic 4
  • 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 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.
Topic 6
  • 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.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

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 # 29
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

  • A. Language Detection
  • B. Text Classification
  • C. Text Generation
  • D. Sentiment Analysis

Answer: C

Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


NEW QUESTION # 30
What is the benefit of using embedding models in OCI Generative AI service?

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

Answer: D

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 # 31
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

  • A. They provide high performance compute resources for fine-tuning tasks.
  • B. They provide faster internet connection speeds.
  • C. They are free of charge for all users.
  • D. They allow access to unlimited database resources.

Answer: A

Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.


NEW QUESTION # 32
What is the difference between classification and regression in Supervised Machine Learning?

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

Answer: D

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 # 33
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