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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q19-Q24):

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

Antwort: D

Begründung:
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.

20. Frage
What is the purpose of the model catalog in OCI Data Science?

A. To provide a preinstalled open source library
B. To store, track, share, and manage models
C. To deploy models as HTTP endpoints
D. To create and switch between different environments

Antwort: B

Begründung:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.

21. Frage
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. Vision
B. Speech
C. Document Understanding
D. Language

Antwort: B

Begründung:
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.

22. Frage
What is " in-context learning " in the realm of Large Language Models (LLMs)?

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

Antwort: A

Begründung:
" 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.

23. Frage
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

Antwort: C

24. Frage
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