Cert Oracle 1Z0-1122-25 Exam, Exam 1Z0-1122-25 Introduction

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
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 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 3	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 4	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 5	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 6	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.

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Audio tuning
B. Timestamping
C. Text normalization
D. Profanity filtering

Answer: C

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 24
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It enhances the visual quality of documents.
B. It provides real-time translation of text.
C. It recognizes and extracts text from a document.
D. It converts audio files into text.

Answer: C

Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

NEW QUESTION # 25
What is the difference between classification and regression in Supervised Machine Learning?

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

Answer: A

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 # 26
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They focus on increasing the number of tokens while keeping the model size constant.
B. They ensure that the model size, training time, and data size are balanced for optimal results.
C. They prioritize larger model sizes to achieve better performance.
D. They disregard model size and prioritize high-quality data only.

Answer: B

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 27
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: C

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 # 28
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Cert Oracle 1Z0-1122-25 Exam, Exam 1Z0-1122-25 Introduction

Quiz 2025 Oracle Pass-Sure Cert 1Z0-1122-25 Exam

Oracle 1Z0-1122-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q23-Q28):

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