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Pass Guaranteed Oracle - 1Z0-1122-25 - Oracle Cloud Infrastructure 2025 AI Foundations Associate Newest Exam Study Solutions

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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

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 5

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 Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which AI domain can be employed for identifying patterns in images and extract relevant features?

A. Speech Processing
B. Anomaly Detection
C. Computer Vision
D. Natural Language Processing

Answer: C

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 11
What is the key feature of Recurrent Neural Networks (RNNs)?

A. They are primarily used for image recognition tasks.
B. They process data in parallel.
C. They have a feedback loop that allows information to persist across different time steps.
D. They do not have an internal state.

Answer: C

Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by "remembering" past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.

NEW QUESTION # 12
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Respect for human autonomy
B. Fairness
C. Explicability
D. Prevention of harm

Answer: C

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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Bottom of Form

NEW QUESTION # 13
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. Supervised learning
D. Active 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 # 14
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They ensure that the model size, training time, and data size are balanced for optimal results.
B. They focus on increasing the number of tokens while keeping the model size constant.
C. They prioritize larger model sizes to achieve better performance.
D. They disregard model size and prioritize high-quality data only.

Answer: A

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 15
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