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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Get started with OCI AI Portfolio	15%	- Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview
Topic 2: Intro to AI Foundations	10%	- Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data
Topic 3: Intro to OCI AI Services	20%	- OCI Speech - OCI Document Understanding - OCI Language - OCI Vision
Topic 4: Intro to Generative AI and LLMs	15%	- Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Transformers Fundamentals - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning
Topic 5: OCI Generative AI and Oracle 23ai	10%	- Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search
Topic 6: Intro to ML Foundations	15%	- Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals 1. Regression 2. Classification - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics
Topic 7: Intro to DL Foundations	15%	- Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN)

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
What is " in-context learning " in the realm of Large Language Models (LLMs)?

A. Teaching a model through zero-shot learning
B. Providing a few examples of a target task via the input prompt
C. Modifying the behavior of a pretrained LLM permanently
D. Training a model on a diverse range of tasks

Answer: B

Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 21
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

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

Answer: D

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 # 22
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

Answer: A

NEW QUESTION # 23
What distinguishes Generative AI from other types of AI?

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

Answer: D

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 # 24
Which capability is supported by Oracle Cloud Infrastructure Language service?

A. Detecting objects and scenes in images
B. Converting text into images
C. Translating text into speech
D. Analyzing text to extract structured information like sentiment or entities

Answer: D

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.

NEW QUESTION # 25
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