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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration
Topic 2: AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI applications, use cases, and responsible AI principles 2. AI, Machine Learning, and Deep Learning relationship
Topic 3: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Convolutional Neural Networks (CNN) architectures 2. Recurrent Neural Networks, LSTMs, and sequence models
Topic 4: Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Reinforcement learning basics and model evaluation concepts 2. Unsupervised learning: clustering and dimensionality reduction 3. Supervised learning: regression and classification
Topic 5: Introduction to OCI AI Services	20%	- OCI AI Service APIs 1. OCI Select AI and AI service use cases 2. OCI Language, Vision, Speech, and Document Understanding services
Topic 6: Generative AI and Large Language Models	15%	- Generative AI concepts 1. Transformers, prompt engineering, and fine-tuning 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
Topic 7: OCI AI Portfolio	15%	- Overview of OCI AI offerings 1. OCI Data Science and GPU-based compute infrastructure 2. AI Services, ML Services, and AI Infrastructure overview

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q19-Q24):

NEW QUESTION # 19
What distinguishes Generative AI from other types of AI?

A. Generative AI focuses on making decisions based on user interactions.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
D. Generative AI involves training models to perform tasks without human intervention.

Answer: C

Explanation:
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.

NEW QUESTION # 20
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

Answer: D

NEW QUESTION # 21
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

NEW QUESTION # 22
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both predict continuous values.
B. Classification and regression both assign data points to categories.
C. Classification predicts continuous values, whereas regression assigns data points to categories.
D. Classification assigns data points to categories, whereas regression predicts continuous values.

Answer: D

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .

NEW QUESTION # 23
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

A. To classify the input image.
B. To generate new images.
C. To detect specific features in the input image.
D. To reduce the spatial dimensions of the input image.

Answer: C

Explanation:
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.

NEW QUESTION # 24
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