1Z0-1122-24 USEFUL DUMPS - FREE PDF QUIZ ORACLE REALISTIC NEW ORACLE CLOUD INFRASTRUCTURE 2024 AI FOUNDATIONS ASSOCIATE TEST REGISTRATION

1z0-1122-24 Useful Dumps - Free PDF Quiz Oracle Realistic New Oracle Cloud Infrastructure 2024 AI Foundations Associate Test Registration

1z0-1122-24 Useful Dumps - Free PDF Quiz Oracle Realistic New Oracle Cloud Infrastructure 2024 AI Foundations Associate Test Registration

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
Topic 2
  • Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
Topic 3
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 4
  • 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.
Topic 5
  • Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
Topic 6
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q34-Q39):

NEW QUESTION # 34
What distinguishes Generative AI from other types of AI?

  • A. Generative AI involves training models to perform tasks without human intervention.
  • B. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
  • C. Generative AI focuses on making decisions based on user interactions.
  • D. Generative AI uses algorithms to predict outcomes based on past data.

Answer: B

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.


NEW QUESTION # 35
What is the primary purpose of reinforcement learning?

  • A. Learning from outcomes to make decisions
  • B. Finding relationships within data sets
  • C. Making predictions from labeled data
  • D. Identifying patterns in data

Answer: A

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


NEW QUESTION # 36
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 # 37
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

  • A. Capturing the internal representation of the raw image data
  • B. Providing labels for the output neurons
  • C. Storing the input pixel values
  • D. Directly predicting the final output

Answer: A

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


NEW QUESTION # 38
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: B

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 # 39
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