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2025 Oracle 1Z0-1122-25 –High-quality Reliable Exam Question

The web-based 1Z0-1122-25 practice exam is similar to the desktop-based software. You can take the web-based 1Z0-1122-25 practice exam on any browser without needing to install separate software. In addition, all operating systems also support this web-based Oracle 1Z0-1122-25 Practice Exam. Both Oracle Cloud Infrastructure 2025 AI Foundations Associate practice exams track your performance and help to overcome mistakes. Furthermore, you can customize your Oracle Cloud Infrastructure 2025 AI Foundations Associate practice exams according to your needs.

Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 (Q25-Q30):

NEW QUESTION # 25
How does AI enhance human efforts?

A. By completely replacing human workers in all tasks
B. By increasing the physical strength of humans
C. By deleting data humans need to handle
D. By processing data at a speed and effectiveness far beyond human capability

Answer: D

Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI's ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.

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

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

Answer: C

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

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

Answer: B

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 # 28
What is the purpose of Attention Mechanism in Transformer architecture?

A. Convert tokens into numerical forms (vectors) that the model can understand.
B. Break down a sentence into smaller pieces called tokens.
C. Weigh the importance of different words within a sequence and understand the context.
D. Apply a specific function to each word individually.

Answer: C

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 29
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. DL is a subset of AI, and ML is a subset of DL.
B. AI, ML, and DL are entirely separate fields with no overlap.
C. AI is a subset of DL, which is a subset of ML.
D. ML is a subset of AI, and DL is a subset of ML.

Answer: D

Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.

NEW QUESTION # 30
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