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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

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

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 # 21
What key objective does machine learning strive to achieve?

A. Enabling computers to learn and improve from experience
B. Creating algorithms to solve complex problems
C. Improving computer hardware
D. Explicitly programming computers

Answer: A

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 22
What is the primary benefit of using the OCI Language service for text analysis?

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

Answer: C

Explanation:
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.

NEW QUESTION # 23
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Text normalization
B. Profanity filtering
C. Audio tuning
D. Timestamping

Answer: A

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 24
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

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

Answer: D

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 # 25
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