Valid 1Z0-1122-25 Exam Prep | Efficient 1Z0-1122-25: Oracle Cloud Infrastructure 2025 AI Foundations Associate 100% Pass

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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.

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Text to speech
B. Audio recording
C. Text summarization
D. Speech recognition

Answer: A

Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is "Text to Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .

NEW QUESTION # 12
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 recognizes and extracts text from a document.
C. It converts audio files into text.
D. It provides real-time translation of text.

Answer: B

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 # 13
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

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

Answer: D

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 14
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. Storing the input pixel values
C. Directly predicting the final output
D. Providing labels for the output neurons

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 # 15
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Encrypting the data for security reasons
B. Upgrading the hardware of the AI clusters
C. Doubling the neural network layers
D. Adjusting the model parameters to improve accuracy

Answer: D

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 16
......

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VALID 1Z0-1122-25 EXAM PREP | EFFICIENT 1Z0-1122-25: ORACLE CLOUD INFRASTRUCTURE 2025 AI FOUNDATIONS ASSOCIATE 100% PASS

Valid 1Z0-1122-25 Exam Prep | Efficient 1Z0-1122-25: Oracle Cloud Infrastructure 2025 AI Foundations Associate 100% Pass