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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: B

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 26
What is the primary benefit of using the OCI Language service for text analysis?

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

Answer: C

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 # 27
What would you use Oracle AI Vector Search for?

A. Manage database security protocols.
B. Query data based on keywords.
C. Query data based on semantics.
D. Store business data in a cloud database.

Answer: C

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 28
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 prioritize larger model sizes to achieve better performance.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: C

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 # 29
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

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

Answer: C

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 # 30
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