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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: C

NEW QUESTION # 40
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

A. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
B. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
C. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
D. Both involve retraining the model, but Prompt Engineering does it more often.

Answer: B

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.

NEW QUESTION # 41
What key objective does machine learning strive to achieve?

A. Explicitly programming computers
B. Creating algorithms to solve complex problems
C. Enabling computers to learn and improve from experience
D. Improving computer hardware

Answer: C

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 42
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. Document Understanding
B. Speech
C. Language
D. Vision

Answer: B

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 # 43
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both predict continuous values.
B. Classification predicts continuous values, whereas regression assigns data points to categories.
C. Classification and regression both assign data points to categories.
D. Classification assigns data points to categories, whereas regression predicts continuous values.

Answer: D

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .

NEW QUESTION # 44
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