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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
- 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 3
- 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 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
- 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.
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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q34-Q39):
NEW QUESTION # 34
Which feature is NOT available as part of OCI Speech capabilities?
- A. Transcribes audio and video files into text
- B. Supports multiple languages including English, Spanish, and Portuguese
- C. Uses extensive data science experience to operate
- D. Provides timestamped, grammatically accurate transcriptions
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 # 35
What would you use Oracle AI Vector Search for?
- A. Query data based on semantics.
- B. Manage database security protocols.
- C. Store business data in a cloud database.
- 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 # 36
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Text Generation
- B. Sentiment Analysis
- C. Text Classification
- D. Language Detection
Answer: A
Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.
NEW QUESTION # 37
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. AI is a subset of DL, which is a subset of ML.
- B. DL is a subset of AI, and ML is a subset of DL.
- C. AI, ML, and DL are entirely separate fields with no overlap.
- D. ML is a subset of AI, and DL is a subset of ML.
Answer: D
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 # 38
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
Answer: A
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 # 39
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