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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI, Machine Learning, and Deep Learning relationship 2. AI applications, use cases, and responsible AI principles
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
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
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration
Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
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
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Reinforcement learning basics and model evaluation concepts 2. Supervised learning: regression and classification 3. Unsupervised learning: clustering and dimensionality reduction

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which feature of OCI Speech helps make transcriptions easier to read and understand?

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

Answer: D

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 # 36
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. Active learning
C. Unsupervised learning
D. Supervised learning

Answer: C

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 # 37
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

Answer: A

Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.

NEW QUESTION # 38
What key objective does machine learning strive to achieve?

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

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 # 39
Which capability is supported by Oracle Cloud Infrastructure Language service?

A. Detecting objects and scenes in images
B. Translating text into speech
C. Analyzing text to extract structured information like sentiment or entities
D. Converting text into images

Answer: C

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.

NEW QUESTION # 40
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