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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Generative AI and Large Language Models	- Large Language Models 1. Language models and generative AI applications 2. LLM fundamentals - Generative AI Fundamentals 1. Generative AI use cases 2. Generative AI concepts and capabilities
Artificial Intelligence and Machine Learning Fundamentals	- Artificial Intelligence Concepts 1. AI fundamentals and terminology 2. AI use cases and applications - Machine Learning Fundamentals 1. Machine learning models and architectures 2. Supervised and unsupervised learning - Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks
OCI Generative AI and Oracle Database AI Capabilities	- Oracle AI Database 1. AI capabilities in Oracle Database 2. Vector database concepts - OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications - AI Application Frameworks 1. Language frameworks 2. Retrieval and AI application concepts
Oracle AI and Machine Learning Services	- OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI - OCI AI Services 1. Speech 2. Language 3. Document Understanding 4. Vision - Oracle AI Stack 1. AI infrastructure 2. AI data and machine learning services

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Fairness
B. Explicability
C. Respect for human autonomy
D. Prevention of harm

Answer: B

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 23
Which is NOT a capability of OCI Vision ' s image analysis?

A. Translating text in images to another language
B. Locating and extracting text in images
C. Assigning classification labels to images
D. Object detection with bounding boxes

Answer: A

Explanation:
OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
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NEW QUESTION # 24
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

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

Answer: D

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 primary purpose of reinforcement learning?

A. Finding relationships within data sets
B. Learning from outcomes to make decisions
C. Identifying patterns in data
D. Making predictions from labeled data

Answer: B

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

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

A. They disregard model size and prioritize high-quality data only.
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 focus on increasing the number of tokens while keeping the model size constant.

Answer: B

NEW QUESTION # 27
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