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Kostenlos 1Z0-1122-25 Dumps Torrent & 1Z0-1122-25 exams4sure pdf & Oracle 1Z0-1122-25 pdf vce

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1Z0-1122-25 Zertifizierungsfragen, 1Z0-1122-25 Fragen Und Antworten

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Oracle 1Z0-1122-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 2

Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.

Thema 3

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.

Thema 4

Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.

Oracle Cloud Infrastructure 2025 AI Foundations Associate 1Z0-1122-25 Prüfungsfragen mit Lösungen (Q30-Q35):

30. Frage
What is the primary purpose of reinforcement learning?

A. Identifying patterns in data
B. Making predictions from labeled data
C. Finding relationships within data sets
D. Learning from outcomes to make decisions

Antwort: D

Begründung:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

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

Antwort: D

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?".

32. Frage
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By generating lifelike speech from documents
B. By automating data extraction from documents
C. By analyzing sentiment in text documents
D. By transcribing spoken language

Antwort: B

Begründung:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.

33. Frage
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Adjusting the model parameters to improve accuracy
B. Upgrading the hardware of the AI clusters
C. Doubling the neural network layers
D. Encrypting the data for security reasons

Antwort: A

Begründung:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

34. Frage
What is the purpose of Attention Mechanism in Transformer architecture?

A. Convert tokens into numerical forms (vectors) that the model can understand.
B. Weigh the importance of different words within a sequence and understand the context.
C. Apply a specific function to each word individually.
D. Break down a sentence into smaller pieces called tokens.

Antwort: B

Begründung:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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35. Frage
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