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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, Machine Learning, and Deep Learning relationship 2. AI applications, use cases, and responsible AI principles
Topic 3: 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 4: Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Supervised learning: regression and classification 2. Reinforcement learning basics and model evaluation concepts 3. Unsupervised learning: clustering and dimensionality reduction
Topic 5: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Topic 6: 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
Topic 7: 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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q25-Q30):

25. Frage
What is the difference between classification and regression in Supervised Machine Learning?

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

Antwort: C

Begründung:
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? " .

26. Frage
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 detect specific features in the input image.
C. To classify the input image.
D. To reduce the spatial dimensions of the input image.

Antwort: B

Begründung:
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.

27. Frage
In machine learning, what does the term " model training " mean?

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

Antwort: C

Begründung:
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.

28. Frage
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

A. Analyzing historical data for unusual patterns
B. Detecting and preventing fraud in financial transactions
C. Generating realistic images from text
D. Detecting vehicle number plates to issue speed citations

Antwort: D

Begründung:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.

29. Frage
What key objective does machine learning strive to achieve?

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

Antwort: D

Begründung:
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.

30. Frage
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