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Exam Oracle 1Z0-1122-25 Revision Plan, Free 1Z0-1122-25 Practice

There is no doubt that advanced technologies are playing an important role in boosting the growth of Oracle companies. This is the reason why the employees have now started upgrading their skillset with the Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) certification exam because they want to work with those latest applications and save their jobs. They attempt the 1Z0-1122-25 exam to validate their skills and try to get their dream job.

Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 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.

Topic 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.

Topic 4

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.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q24-Q29):

NEW QUESTION # 24
What is the difference between classification and regression in Supervised Machine Learning?

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

Answer: B

Explanation:
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?".

NEW QUESTION # 25
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Unsupervised learning
B. Reinforcement learning
C. Active learning
D. Supervised learning

Answer: A

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 # 26
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 offers seamless integration with social media platforms.
C. It provides a cost-effective solution for simple AI tasks.
D. It is ideal for tasks such as text-to-speech conversion.

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 # 27
What does "fine-tuning" refer to in the context of OCI Generative AI service?

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

Answer: C

Explanation:
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.

NEW QUESTION # 28
Which feature is NOT available as part of OCI Speech capabilities?

A. Transcribes audio and video files into text
B. Provides timestamped, grammatically accurate transcriptions
C. Supports multiple languages including English, Spanish, and Portuguese
D. Uses extensive data science experience to operate

Answer: D

Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs. These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.

NEW QUESTION # 29
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