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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q41-Q46):

NEW QUESTION # 41
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Text to speech
B. Text summarization
C. Audio recording
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 # 42
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

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

Answer: A

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 # 43
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

A. It is ideal for tasks such as text-to-speech conversion.
B. It offers seamless integration with social media platforms.
C. It delivers exceptional performance and scalability for complex AI tasks.
D. It provides a cost-effective solution for simple AI tasks.

Answer: C

NEW QUESTION # 44
What is the purpose of the model catalog in OCI Data Science?

A. To deploy models as HTTP endpoints
B. To create and switch between different environments
C. To store, track, share, and manage models
D. To provide a preinstalled open source library

Answer: C

Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.

NEW QUESTION # 45
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: C

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 # 46
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