USEFUL 1Z0-1122-24 DUMPS, 1Z0-1122-24 RELIABLE PRACTICE QUESTIONS

Useful 1z0-1122-24 Dumps, 1z0-1122-24 Reliable Practice Questions

Useful 1z0-1122-24 Dumps, 1z0-1122-24 Reliable Practice Questions

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
Topic 2
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 3
  • Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is the purpose of the model catalog in OCI Data Science?

  • A. To deploy models as HTTP endpoints
  • B. To store, track, share, and manage models
  • C. To create and switch between different environments
  • D. To provide a preinstalled open source library

Answer: B

Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.


NEW QUESTION # 15
What is the primary benefit of using the OCI Language service for text analysis?

  • A. It allows for text analysis at scale without machine learning expertise.
  • B. It requires extensive machine learning expertise to use.
  • C. It only works with structured data.
  • D. It provides image processing capabilities.

Answer: A

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


NEW QUESTION # 16
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

  • A. Detecting vehicle number plates to issue speed citations
  • B. Analyzing historical data for unusual patterns
  • C. Detecting and preventing fraud in financial transactions
  • D. Generating realistic images from text

Answer: A

Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.


NEW QUESTION # 17
What does "fine-tuning" refer to in the context of OCI Generative AI service?

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

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 # 18
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

  • A. AI, ML, and DL are entirely separate fields with no overlap.
  • B. ML is a subset of AI, and DL is a subset of ML.
  • 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: B

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 # 19
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