Real Oracle 1z0-1122-26 PDF Questions - Great Tips

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: OCI Generative AI and Oracle Database AI Capabilities	- 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 Database 1. Vector database concepts 2. AI capabilities in Oracle Database
Topic 2: Oracle AI and Machine Learning Services	- OCI AI Services 1. Language 2. Speech 3. Document Understanding 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
Topic 3: Artificial Intelligence and Machine Learning Fundamentals	- Deep Learning Fundamentals 1. Neural networks 2. Convolutional and sequence models - 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
Topic 4: 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 concepts and capabilities 2. Generative AI use cases

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Reinforcement learning
B. Unsupervised learning
C. Active learning
D. Supervised learning

Answer: B

Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

NEW QUESTION # 49
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: A

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 50
In machine learning, what does the term " model training " mean?

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

Answer: B

Explanation:
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.

NEW QUESTION # 51
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It enhances the visual quality of documents.
B. It provides real-time translation of text.
C. It recognizes and extracts text from a document.
D. It converts audio files into text.

Answer: C

Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI ' s OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

NEW QUESTION # 52
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. Reinforcement Learning
B. Retrieval-Augmented Generation (RAG)
C. Zero-shot Prompting
D. Prompt Engineering

Answer: B

Explanation:
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.

NEW QUESTION # 53
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