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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

A. Computer Vision
B. Natural Language Processing
C. Anomaly Detection
D. Natural Language Processing

Answer: D

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 32
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: D

Explanation:
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.

NEW QUESTION # 33
What does " fine-tuning " refer to in the context of OCI Generative AI service?

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

Answer: D

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 # 34
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They allow access to unlimited database resources.
B. They are free of charge for all users.
C. They provide high performance compute resources for fine-tuning tasks.
D. They provide faster internet connection speeds.

Answer: C

Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

NEW QUESTION # 35
Which is NOT a capability of OCI Vision ' s image analysis?

A. Assigning classification labels to images
B. Locating and extracting text in images
C. Translating text in images to another language
D. Object detection with bounding boxes

Answer: C

Explanation:
OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
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NEW QUESTION # 36
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