ACTUAL ORACLE 1Z0-1122-24 EXAM QUESTIONS

Actual Oracle 1z0-1122-24 Exam Questions

Actual Oracle 1z0-1122-24 Exam Questions

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • 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 6
  • 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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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

Answer: C

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 # 28
Which capability is supported by Oracle Cloud Infrastructure Language service?

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

Answer: B

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 # 29
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Translation models
  • B. Chat models
  • C. Embedding models
  • D. Generation models

Answer: A

Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


NEW QUESTION # 30
Which is NOT a capability of OCI Vision's image analysis?

  • A. Assigning classification labels to images
  • B. Translating text in images to another language
  • C. Locating and extracting text in images
  • D. Object detection with bounding boxes

Answer: B

Explanation:
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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NEW QUESTION # 31
What is "in-context learning" in the realm of Large Language Models (LLMs)?

  • A. Modifying the behavior of a pretrained LLM permanently
  • B. Providing a few examples of a target task via the input prompt
  • C. Training a model on a diverse range of tasks
  • D. Teaching a model through zero-shot learning

Answer: B

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 # 32
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