100% PASS QUIZ ORACLE - 1Z0-1122-24 THE BEST TEST RESULT

100% Pass Quiz Oracle - 1z0-1122-24 The Best Test Result

100% Pass Quiz Oracle - 1z0-1122-24 The Best Test Result

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 2
  • 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 3
  • 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 4
  • 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.

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1z0-1122-24 Reliable Dumps, 1z0-1122-24 Test Study Guide

We provide the Oracle 1z0-1122-24 exam questions in a variety of formats, including a web-based practice test, desktop practice exam software, and downloadable PDF files. ExamcollectionPass provides proprietary preparation guides for the certification exam offered by the 1z0-1122-24 Exam Dumps. In addition to containing numerous questions similar to the 1z0-1122-24 exam, the Oracle Cloud Infrastructure 2024 AI Foundations Associate (1z0-1122-24) exam questions are a great way to prepare for the Oracle 1z0-1122-24 exam dumps.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is the primary purpose of reinforcement learning?

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

Answer: C

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 # 12
What would you use Oracle AI Vector Search for?

  • A. Store business data in a cloud database.
  • B. Query data based on keywords.
  • 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 # 13
What distinguishes Generative AI from other types of AI?

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

Answer: C

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 # 14
Which feature is NOT available as part of OCI Speech capabilities?

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

Answer: D

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

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

Answer: B

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
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