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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.

Topic 2

Intro to DL Foundations: This section assesses the expertise of Deep Learning Engineers in understanding deep learning frameworks and architectures. It covers fundamental concepts of deep learning, introduces convolutional neural networks (CNN) for image processing, and explores sequence models like recurrent neural networks (RNN) and long short-term memory (LSTM) networks for handling sequential data.

Topic 3

Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.

Topic 4

OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.

Topic 5

Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which capability is supported by Oracle Cloud Infrastructure Language service?

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

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 # 39
What would you use Oracle AI Vector Search for?

A. Query data based on semantics.
B. Store business data in a cloud database.
C. Manage database security protocols.
D. Query data based on keywords.

Answer: A

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 40
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

A. Backpropagation
B. Gradient Descent
C. Random Forest
D. Support Vector Machine

Answer: A

Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.

NEW QUESTION # 41
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

A. Directly predicting the final output
B. Providing labels for the output neurons
C. Storing the input pixel values
D. Capturing the internal representation of the raw image data

Answer: D

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.

NEW QUESTION # 42
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

Answer: D

Explanation:
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.

NEW QUESTION # 43
......

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