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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which feature of OCI Speech helps make transcriptions easier to read and understand?

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

Answer: B

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

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

Answer: D

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
What is the benefit of using embedding models in OCI Generative AI service?

A. They enable creating detailed graphics.
B. They simplify managing databases.
C. They optimize the use of computational resources.
D. They facilitate semantic searches.

Answer: D

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .

NEW QUESTION # 47
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: A

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 # 48
What is the primary benefit of using the OCI Language service for text analysis?

A. It only works with structured data.
B. It provides image processing capabilities.
C. It requires extensive machine learning expertise to use.
D. It allows for text analysis at scale without machine learning expertise.

Answer: D

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 49
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