1Z0-1122-24 CERTIFICATION - PREMIUM 1Z0-1122-24 FILES

1z0-1122-24 Certification - Premium 1z0-1122-24 Files

1z0-1122-24 Certification - Premium 1z0-1122-24 Files

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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 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.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which capability is supported by Oracle Cloud Infrastructure Language service?

  • A. Analyzing text to extract structured information like sentiment or entities
  • B. Converting text into images
  • C. Detecting objects and scenes in images
  • D. Translating text into speech

Answer: A

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


NEW QUESTION # 43
What feature of OCI Data Science provides an interactive coding environment for building and training models?

  • A. Accelerated Data Science (ADS) SDK
  • B. Notebook sessions
  • C. Model catalog
  • D. Conda environment

Answer: B

Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.


NEW QUESTION # 44
What is the benefit of using embedding models in OCI Generative AI service?

  • A. They simplify managing databases.
  • B. They optimize the use of computational resources.
  • C. They facilitate semantic searches.
  • D. They enable creating detailed graphics.

Answer: C

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .


NEW QUESTION # 45
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

  • A. It provides a cost-effective solution for simple AI tasks.
  • B. It is ideal for tasks such as text-to-speech conversion.
  • C. It offers seamless integration with social media platforms.
  • D. It delivers exceptional performance and scalability for complex AI tasks.

Answer: D

Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.


NEW QUESTION # 46
What key objective does machine learning strive to achieve?

  • A. Explicitly programming computers
  • B. Enabling computers to learn and improve from experience
  • C. Creating algorithms to solve complex problems
  • D. Improving computer hardware

Answer: B

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


NEW QUESTION # 47
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