Echte 1z0-1122-26 Fragen und Antworten der 1z0-1122-26 Zertifizierungsprüfung

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle 1z0-1122-26 Praxisprüfung & 1z0-1122-26 Prüfungsaufgaben

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

19. Frage
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

A. Retrieval-Augmented Generation (RAG)
B. Zero-shot Prompting
C. Prompt Engineering
D. Reinforcement Learning

Antwort: A

Begründung:
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.

20. Frage
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 focuses on making decisions based on user interactions.
D. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.

Antwort: D

Begründung:
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.

21. Frage
What is the primary benefit of using the OCI Language service for text analysis?

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

Antwort: B

Begründung:
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.

22. Frage
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They prioritize larger model sizes to achieve better performance.
B. They ensure that the model size, training time, and data size are balanced for optimal results.
C. They disregard model size and prioritize high-quality data only.
D. They focus on increasing the number of tokens while keeping the model size constant.

Antwort: B

Begründung:
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.

23. Frage
In machine learning, what does the term " model training " mean?

A. Analyzing the accuracy of a trained model
B. Writing code for the entire program
C. Establishing a relationship between input features and output
D. Performing data analysis on collected and labeled data

Antwort: C

Begründung:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

24. Frage
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