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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q18-Q23):

NEW QUESTION # 18
What does " fine-tuning " refer to in the context of OCI Generative AI service?

A. Upgrading the hardware of the AI clusters
B. Adjusting the model parameters to improve accuracy
C. Doubling the neural network layers
D. Encrypting the data for security reasons

Answer: B

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 19
Which feature is NOT available as part of OCI Speech capabilities?

A. Supports multiple languages including English, Spanish, and Portuguese
B. Provides timestamped, grammatically accurate transcriptions
C. Uses extensive data science experience to operate
D. Transcribes audio and video files into text

Answer: C

Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.

NEW QUESTION # 20
In machine learning, what does the term " model training " mean?

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

Answer: C

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 # 21
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 focus on increasing the number of tokens while keeping the model size constant.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They disregard model size and prioritize high-quality data only.

Answer: C

NEW QUESTION # 22
A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?

A. Sentiment analysis
B. Named entity recognition
C. Key phrase extraction
D. Language detection

Answer: A

Explanation:
OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.

NEW QUESTION # 23
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