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[General] Take Your Exam Preparation to the Next Level with Exams4Collection Oracle 1Z0-11

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【General】 Take Your Exam Preparation to the Next Level with Exams4Collection Oracle 1Z0-11

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Oracle 1Z0-1122-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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 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 4
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q36-Q41):NEW QUESTION # 36
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
  • A. Text Classification
  • B. Text Generation
  • C. Language Detection
  • D. Sentiment Analysis
Answer: B
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 AI domain can be employed for identifying patterns in images and extract relevant features?
  • A. Computer Vision
  • B. Anomaly Detection
  • C. Natural Language Processing
  • D. Speech Processing
Answer: A
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 # 38
What is "in-context learning" in the realm of Large Language Models (LLMs)?
  • A. Teaching a model through zero-shot learning
  • B. Training a model on a diverse range of tasks
  • C. Modifying the behavior of a pretrained LLM permanently
  • D. Providing a few examples of a target task via the input prompt
Answer: D
Explanation:
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 39
What would you use Oracle AI Vector Search for?
  • A. Query data based on keywords.
  • B. Store business data in a cloud database.
  • C. Manage database security protocols.
  • D. Query data based on semantics.
Answer: D
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 # 40
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
  • A. Natural Language Processing
  • B. Anomaly Detection
  • C. Natural Language Processing
  • D. Computer Vision
Answer: A
Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 41
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