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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: OCI Generative AI and Oracle Database AI Capabilities	- AI Application Frameworks 1. Language frameworks 2. Retrieval and AI application concepts - OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications - Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database
Topic 3: Artificial Intelligence and Machine Learning Fundamentals	- 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 - Artificial Intelligence Concepts 1. AI fundamentals and terminology 2. AI use cases and applications
Topic 4: Oracle AI and Machine Learning Services	- OCI AI Services 1. Document Understanding 2. Language 3. Vision 4. Speech - Oracle AI Stack 1. AI data and machine learning services 2. AI infrastructure - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q31-Q36):

NEW QUESTION # 31
What distinguishes Generative AI from other types of AI?

A. Generative AI involves training models to perform tasks without human intervention.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
D. Generative AI focuses on making decisions based on user interactions.

Answer: C

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

NEW QUESTION # 32
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

A. Speech
B. Document Understanding
C. Language
D. Vision

Answer: A

Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

NEW QUESTION # 33
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 disregard model size and prioritize high-quality data only.
C. They focus on increasing the number of tokens while keeping the model size constant.
D. They prioritize larger model sizes to achieve better performance.

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 # 34
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

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 # 35
What is the benefit of using embedding models in OCI Generative AI service?

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

Answer: B

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 # 36
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