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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 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 3	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 4	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.
Topic 5	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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1Z0-1122-25 test torrent, exam questions and answers for Oracle Cloud Infrastructure 2025 AI Foundations Associate

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q40-Q45):

NEW QUESTION # 40
What key objective does machine learning strive to achieve?

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

Answer: D

Explanation:
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.

NEW QUESTION # 41
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. ML is a subset of AI, and DL is a subset of ML.
B. AI, ML, and DL are entirely separate fields with no overlap.
C. AI is a subset of DL, which is a subset of ML.
D. DL is a subset of AI, and ML is a subset of DL.

Answer: A

Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.

NEW QUESTION # 42
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Model catalog
B. Accelerated Data Science (ADS) SDK
C. Conda environment
D. Notebook sessions

Answer: D

Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.

NEW QUESTION # 43
Which feature is NOT available as part of OCI Speech capabilities?

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

Answer: C

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 # 44
What is the purpose of Attention Mechanism in Transformer architecture?

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

Answer: C

Explanation:
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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NEW QUESTION # 45
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1Z0-1122-25 Valid Study Materials & 1Z0-1122-25 Valid Exam Online

Oracle 1Z0-1122-25 Exam Syllabus Topics:

1Z0-1122-25 test torrent, exam questions and answers for Oracle Cloud Infrastructure 2025 AI Foundations Associate

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q40-Q45):

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